CyberThreat-Insight¶
Anomalous Behavior Detection in Cybersecurity Analytics using Generative AI
Toronto, November 01 2024
Autor : Atsu Vovor
Master of Management in Artificial Intelligence
Consultant Data Analytics Specialist | Machine Learning |
Data science | Quantitative Analysis |French & English Bilingual
Abstract¶
The CyberThreat Insight project leverages data analytics and machine learning to detect and analyze anomalous behavior in user accounts and network systems. Using synthetic data generated through advanced augmentation techniques, the project investigates patterns in cybersecurity issues, enabling proactive threat detection and response. This research-driven approach provides actionable intelligence, that can help organizations reduce risk from internal and external threats.
This project is a research-focused initiative aimed at exploring the potential of generative algorithms in cybersecurity analytics. The methods implemented are designed to simulate data that emulate real-world cyberattack scenarios. It is important to note that the data used in this project is entirely synthetic, with no initial dataset sourced externally for baseline reference.
Introduction¶
In today’s evolving cybersecurity landscape, identifying subtle and anomalous behaviors is essential for combating sophisticated cyber threats. The CyberThreat Insight project aims to harness machine learning to understand and address complex cybersecurity challenges. By analyzing synthetic data that mirrors real-world cybersecurity issues, this project will identify unusual behaviors such as high login attempts, extended session durations, or significant data transfers. The findings will support organizations in developing proactive detection capabilities, improving their ability to respond swiftly to internal and external threats.
Project Description¶
The CyberThreat Insight project will focus on the following key areas to build an anomaly detection framework for cybersecurity analytics:
Research and Analysis Objectives: This project is designed for research and analysis purposes, investigating how machine learning techniques can enhance understanding and detection of complex cybersecurity issues. By identifying patterns that signify potential threats, the project is intended to improve decision-making and support risk mitigation.
Synthetic Data Generation: Using data augmentation techniques—such as SMOTE, GANs, label shuffling, time-series variability, and noise addition—the project will create a synthetic dataset with realistic, month-over-month volatility. This data will include anomalies that reflect potential security concerns, such as unusually high login attempts, extended session durations amd large data transfer volumes.
Anomaly Detection with Machine Learning: Machine learning models will be applied to identify and classify unusual patterns within the dataset. Techniques like Isolation Forests, Autoencoders, and DBSCAN will help in detecting anomalies, enabling the system to pinpoint behaviors that deviate from established baselines.
Proactive Threat Detection and Response: The project will integrate these models with alerting mechanisms, providing security teams with actionable insights for early threat response. By identifying suspicious activity patterns in real-time, the system will offer timely intelligence for mitigating internal and external threats.
Continuous Model Improvement: Feedback from detection results and analysts’ input will be incorporated to refine models, ensuring that they adapt to emerging threat patterns and reduce false positives.
Project Outcome and Impact: The final deliverable will be an anomaly detection framework capable of analyzing user behaviors and system interactions, alerting security teams to potentially malicious activities. By proactively identifying threats, the CyberThreat Insight project will help organizations enhance their cybersecurity resilience, gaining valuable insights for future threat prevention.
Scope of the Project¶
1.Data Preparation( Data synthetization & Preprocessing)¶
In this section, we will use data augmentation techniques (SMOTE, GAN, label shuffling or permutation, time series variability, and noise addition) to generate a synthetic cybersecurity issues dataset that will include month-to-month volatility and significant anomalies (such as high login attempts, unusual session durations, or high data transfer volumes). The goal here is to reduce the umbalance of data classes.
shouldn't it be better to generate # Create anomalous issues dataset def generate_anomalous_issues_df(p_anomalous_issue_ids, p_anomalous_issue_keys) module based on the generate_normal_issues_df output( generate_normal_issues_df output to be used as imput to generate anomalous issues dataset)? I think this will help to customize or ajuste the anomalous or update the columns included in the anomalous when there is not enougt anomalous.
absolutely. That’s a great idea, and it’ll give you more flexibility and realism in your dataset. By basing the anomalous issues generation on the normal issues dataset, you:
Benefits Ensure Column Consistency Any schema changes to the normal dataset automatically propagate.
Simplify Maintenance No need to manage column lists or logic redundantly in two places.
Customizable Anomalies You can select rows from the normal set and tweak them (e.g., elevate threat metrics, distort values).
Guarantee Coverage Especially useful when you don’t have enough anomalies — you can "morph" some normal rows into anomalies.
Support Semi-Synthetic Modeling Which is closer to real-world threats — abnormal behavior typically starts from a normal baseline.
Core Data Schema: Each column will be structured to simulate real-world attributes.
Issue ID,Issue Key: Unique identifiers.Issue Name,Category,Severity: Descriptive issue metadata with categorical values.Status,Reporters,Assignees: Status categories and personnel involved.Date Reported,Date Resolved: Randomized dates across a timeline.Impact Score,Risk Level: Randomized scores to reflect varying severity.Cost: Randomized to reflect the volatility in month-over-month impact.
User Activity Columns:
Columns like user_id, timestamp, activity_type, location, session_duration, and data_transfer_MB will be generated to simulate behavioral patterns.
Monthly Volatility:
- Impact Score, Cost, and data_transfer_MB We use synthetic techniques to create spikes or drops in activity between months, simulating the volatility in issues or user activity.
- For example, we use random walks to vary values in a non-linear fashion to capture realistic volatility.
Data Augmentation:
- Scaling Up Data Points: We will use SMOTE or random sampling for categorical columns to add diversity.
- Label Swapping for
Assignees,Departments: Here, we randomly reassign categories periodically to simulate changing roles. - Time-Series Variability: We use simulated timestamps within and across sessions to show login attempts, data transfer spikes, and session durations.
User activity features:
- user_id: Identifier for each user.
- timestamp: Time of the activity.
- activity_type: Type of activity (e.g., "login," "file_access," "data_modification").
- location: User's location (e.g., IP region).
- session_duration: Length of session in seconds.
- num_files_accessed: Number of files accessed in a session.
- ogin_attempts: Number of login attempts in a session.
- data_transfer_MB: Amount of data transferred (MB).
Anomalies:
- We include some rows with anomalous patterns like high login attempts, unusual session duration and high data transfer volumes from unexpected locations
Explanation of Key Parts:
Volatile Data Generation: The
generate_volatile_datafunction adds random fluctuations to values, simulating high month-over-month volatility.User Activity Features: Columns like
activity_type,session_duration,num_files_accessed,login_attempts, anddata_transfer_MBare varied to reflect real user behaviors.Random Timestamps: Activity timestamps are spread across the timeline from
start_datetoend_date.Generate normal issues dataset: First, we a normal issue dataset with almost no data anomaly
Generate anomalous issues dataset: The we introduce anomaly to the detaset
Combine normal and anomalous data: We combine both normal and anomalous datasets
Adressing class imbalance in datasets:Using SMOTE (Synthetic Minority Over-sampling Technique) we make sure that class imbalance in the dataset is resolved.
all the data files are saves on google drive
User Activities Generation Metrics Formula
The expression: base_value + base_value * volatility * (np.random.randn()) * (1.2 if severity in ['High', 'Critical'] else 1)
means that we’re generating a value based on a starting point (base_value) and adjusting it for both randomness and severity level. Here's a breakdown:
- base_value: This is the initial value that the output is based on.
- volatility * (np.random.randn()): This part adds a random fluctuation around the base_value.
np.random.randn()generates a value from a standard normal distribution (centered around 0), so it could be positive or negative, creating variation. Multiplying by volatility scales the randomness, making the fluctuation stronger or weaker. - (1.2 if severity in ['High', 'Critical'] else 1): This adds an additional factor to increase the outcome by 20% if the severity is "High" or "Critical." If severity isn’t in these categories, the factor is simply 1, meaning no extra adjustment.
So, if severity is "High" or "Critical," the result is a base value adjusted for both volatility and severity; otherwise, it’s just the base value with volatility adjustment.
Treat level Identification and Adaptive Defense Systems Setting
We will set up a threat level based our cybersecurity dataset generated. We will create a threat scoring model that combines multiple relevant features.
Key Threat Indicators (KTIs) Definition
The following columns will be uses as key threat indicators (KTIs):
- Severity: Indicates the criticality of the issue.
- Impact Score: Represents the potential damage if the threat is realized.
- Risk Level: A general indicator of risk associated with each issue.
- Issue Response Time Days: The longer it takes to respond, the higher the threat level could be.
- Category: Certain categories (e.g., unauthorized access) carry a higher base threat level.
- Activity Type: Suspicious activity types (e.g., high login attempts, data modification) indicate a greater threat.
- Login Attempts: Unusually high login attempts signal a brute force attack.
- Num Files Accessed and Data Transfer MB: Large data transfers or access to many files in a session could indicate data exfiltration or suspicious activity.
KTIs based Scoring
For each KTI we will define the acriteria to be used to assigne a score
| KTI | Condition | Score |
|---|---|---|
| Severity | Critical = 10, High = 8, Medium = 5, Low = 2 | 2 - 10 |
| Impact Score | 1 to 10 (already a score) | 1 - 10 |
| Risk Level | High = 8, Medium = 5, Low = 2 | 2 - 8 |
| Response Time | >7 days = 5, 3-7 days = 3, <3 days = 1 | 1 - 5 |
| Category | Unauthorized Access = 8, Phishing = 6, etc. | 1 - 8 |
| Activity Type | High-risk types (e.g., login, data_transfer) | 1 - 5 |
| Login Attempts | >5 = 5, 3-5 = 3, <3 = 1 | 1 - 5 |
| Num Files Accessed | >10 = 5, 5-10 = 3, <5 = 1 | 1 - 5 |
| Data Transfer MB | >100 MB = 5, 50-100 MB = 3, <50 MB = 1 | 1 - 5 |
Threat Score Calculation The threat level is calculated as a weighted sum of these scores. For example:
Threat Score = 0.3 × Severity + 0.2 × Impact Score + 0.2 × Risk Level + 0.1 × Response Time + 0.1 × Login Attempts + 0.05 × Num Files Accessed + 0.05 × Data Transfer MB
Note: The weights could be adjusted based on the importance of each factor in your specific cybersecurity context.
Threat Level Thresholds Definition
We use the final threat score to categorize the threat level:
- Low Threat: 0–3
- Medium Threat: 4–6
- High Threat: 7–9
- Critical Threat: 10+
Real-Time Calculation and Monitoring Implementation To implement this dynamically we :
- Calculate and log the threat score whenever new data is added.
- Set up alerts for high and critical threat scores.
- Integrate this scoring model into a real-time dashboard or cybersecurity scorecard.
This method provides a structured and quantifiable approach to assessing the threat level based on multiple relevant indicators from the initial dataset.
Rule-based Adaptive Defense Mechanism
Here we will add logic that monitors specific threat conditions in real-time and adapt responses based on defined rules. This will include automatic flagging of high-threat issues, increasing logging frequency for suspicious activities, and assigning specific mitigation actions based on the threat level and activity context.
Rules Definition
We will use the following features to define rules that will be applied to identify potential threats and recommend defensive actions: Threat Level, Severity, Impact Score, Login Attempts, Risk Level, Issue Response Time Days, Num Files Accessed,Data Transfer MB.
Defense Mechanism: The system will respond adaptively by adding flags and assigning custom actions based on the rule evaluations and scenarios colors
The defense mechanism assigns an adaptive Defense Action to each issue based on threat conditions, adding an extra layer of automated response for varying threat levels and behaviors.
The treat conditions are implemented by Color-coding cybersecurity scenarios, we bealieve, is a helpful way to quickly communicate risk levels and prioritize response actions. Here's a suggested approach to buld the scenarios, where we use intensity of red, orange, yellow, and green to represent risk:
Color Scheme
- Critical Threat & Severity: Dark Red – Highest urgency.
- High Threat or Severity: Orange – Serious, but not the highest urgency.
- Medium Threat or Severity: Yellow – Moderate concern.
- Low Threat & Severity: Green – Low concern, monitor as needed.
Scenarios with Colors
| Scenario | Threat Level | Severity | Suggested Color | Rationale |
|---|---|---|---|---|
| 1 | Critical | Critical | Dark Red | Maximum urgency, both threat and impact are critical. Immediate action required. |
| 2 | Critical | High | Red | Very high risk, threat is critical and impact is significant. Prioritize response. |
| 3 | Critical | Medium | Orange-Red | Significant threat but moderate impact. Act promptly to prevent escalation. |
| 4 | Critical | Low | Orange | High potential risk, current impact is minimal. Monitor closely and mitigate quickly. |
| 5 | High | Critical | Red | High threat combined with critical impact. Needs immediate action. |
| 6 | High | High | Orange-Red | High threat and significant impact. Prioritize response. |
| 7 | High | Medium | Orange | Elevated threat and moderate impact. Requires attention. |
| 8 | High | Low | Yellow-Orange | High threat with low impact. Proactive monitoring recommended. |
| 9 | Medium | Critical | Orange | Moderate threat with critical impact. Prioritize addressing the severity. |
| 10 | Medium | High | Yellow-Orange | Medium threat with high impact. Needs resolution soon. |
| 11 | Medium | Medium | Yellow | Medium threat and impact. Plan to address it. |
| 12 | Medium | Low | Light Yellow | Moderate threat, minimal impact. Monitor as needed. |
| 13 | Low | Critical | Yellow | Low threat but high impact. Address severity first. |
| 14 | Low | High | Light Yellow | Low threat with significant impact. Plan mitigation. |
| 15 | Low | Medium | Green-Yellow | Low threat, moderate impact. Routine monitoring. |
| 16 | Low | Low | Green | Minimal risk. No immediate action required. |
This color based scenarios approach aligns urgency with the dual factors of threat level and severity, ensuring quick comprehension and appropriate prioritization.
2. Explanatory Data Analysis(EDA)¶
The following steps were implemented in the exploratory data analysis (EDA) pipeline to analyze the dataset's key features and distribution patterns:
Data Normalization:
- Implemented a function to normalize numerical features using Min-Max Scaling for consistent feature scaling.
Time-Series Visualization:
- Plotted daily distribution of numerical features pre- and post-normalization using line plots for visualizing trends over time.
Statistical Feature Analysis:
- Developed histograms and boxplots for all features, including overlays of statistical metrics (mean, standard deviation, skewness, kurtosis) for numerical features.
- Integrated risk levels with customized color palettes for categorical data.
Scatter Plot and Correlation Analysis:
- Created scatter plots to analyze relationships between key features such as session duration, login attempts, data transfer, and user location.
- Generated a correlation heatmap to visualize interdependencies among numerical features.
Distribution Analysis Pipeline:
- Built a modular pipeline to evaluate and compare the distribution of activity features across daily and aggregated reporting frequencies (e.g., monthly, quarterly).
Comprehensive Feature Analysis:
- Combined scatter plots, heatmaps, and distribution visualizations into a unified framework for insights into user behavior and feature relationships.
Dynamic Layouts and Annotations:
- Optimized subplot layouts to handle a variable number of features and annotated plots with key statistics for enhanced interpretability.
This pipeline provides a detailed understanding of numerical and categorical feature behaviors while highlighting correlations and potential anomalies in the dataset.
3. Features Engineering Pipeline¶
The feature engineering pipeline was designed to simulate realistic cybersecurity scenarios, enhance anomaly detection, and prepare the dataset for effective model training. It involved the following key steps:
- Synthetic Data Load: Real-time behavioral data was simulated to represent normal system activity.
- Anomaly Injection (Cholesky Perturbation): Statistically realistic anomalies were introduced to compensate for the natural scarcity of threat events.
- Feature Normalization: All features were scaled using Min-Max and Z-score methods to ensure consistent input ranges.
- Correlation Analysis: Pearson and Spearman heatmaps helped identify and mitigate multicollinearity among variables.
- Feature Importance (Random Forest): The most influential threat indicators were identified for model optimization.
- Model Explainability (SHAP): SHAP values provided interpretability for each prediction, essential for SOC analysts.
- Dimensionality Reduction (PCA): Principal Component Analysis reduced noise while preserving important behavioral patterns.
- Data Augmentation (SMOTE + GANs): Oversampling techniques balanced the dataset by generating synthetic threat instances.
This workflow produced a clean, balanced, and interpretable feature set optimized for machine learning–based cyber threat classification.
4. Train-Test Split¶
A function is defined to split the augmented feature matrix and target vector
Assign the results to variables representing the training and testing data```
Define a function for splitting the dataset into training and testing subsets
Use train_test_split from sklearn to randomly split the data
test_size=0.2 specifies that 20% of the data will be allocated to the testing set.
5. Anomaly Detection Models Developement¶
We implemented two supervised machine learning algorithms(Random Forest Gradient Boosting), six unsupervised machine learning algorithms(Isolation Forest One-Class SVM, DBSCAN, Autoencoder, K-means Clustering, Local Outlier Factor (LOF)) and one mixed superviced and unsupervised machine learning algorithm(LSTM (Long Short-Term Memory))
6. Best model Selection¶
We chosed the most performing algorithm based on each model 'Overall Model Accuracy'.
7. Best Model Deployment¶
We deployed the winning medel to myGoogle drive.
Through this systematic approach, CyberThreat Insight will contribute to a deeper understanding of behavioral anomalies, equipping organizations with the tools needed to anticipate and mitigate cybersecurity risks effectively.
8. Best Model Testing¶
As testing strategy, we will uploard the best model and run it with the inital synthetic data that serve as real time production data. The main reason of using the initial synthetic data is that the model was developed using augmented data. The purpuse of our testing strategy is to capture the model performance on the real data.
We will run the model performance visualization charts like:
- Squater Plot on y = Data Transfer, X = Session Duration
- ROC curve(Y=True Positive Rate, X= false positive rate)
- Precision recall curve(y= precition, X= recall)
As part of our testing strategy, we will evaluate the best-performing model using the initial synthetic dataset. This dataset simulates real-time production environments and is independent of the augmented data used during training. This approach allows us to evaluate how well the model generalizes to operational-like conditions and to identify any overfitting to the augmented training data.
Evaluation Metrics¶
We will generate the following performance outputs and charts to interpret model behavior across all threat levels:
1. Confusion Matrix¶
Purpose: Visualize how well the model classifies each threat category (
Threat Level).Interpretation: Shows the counts of true vs. predicted labels.
- Diagonal values = correct classifications
- Off-diagonal values = misclassifications
Helps Identify:
- Whether the model is confusing High with Medium threats, etc.
- If there's any class imbalance affecting performance
2. Classification Report¶
Includes:
- Precision: How many predicted labels were actually correct?
- Recall: How many true labels were correctly predicted?
- F1-score: Harmonic mean of precision and recall
- Support: Number of actual instances per class
Purpose: Detailed per-class evaluation — crucial for cybersecurity, where missing a high threat is more costly than misclassifying a low threat.
3. ROC Curve¶
- X-axis: False Positive Rate
- Y-axis: True Positive Rate (Recall)
- Multiclass: Will be plotted using a One-vs-Rest strategy
- Purpose: Shows how well the model distinguishes between each threat level at different thresholds
4. Precision-Recall Curve¶
- X-axis: Recall
- Y-axis: Precision
- Multiclass: One-vs-Rest approach
- Purpose: Ideal for imbalanced classes (e.g., rare high-risk attacks)
- Key Insight: Focus on how well the model maintains precision as it tries to improve recall
5. Scatter Plot¶
- X-axis:
Session Duration in Second - Y-axis:
Data Transfer MB - Color: Model-predicted
Threat Level - Purpose: Visual exploratory view to see how predicted threat levels distribute across session metrics
Feature Set Used¶
| Feature | Description |
|---|---|
Issue Response Time Days |
How long it took to respond to the issue |
Impact Score |
Estimated impact of the session |
Cost |
Operational or financial impact |
Session Duration in Second |
Length of the session |
Num Files Accessed |
Number of files accessed during session |
Login Attempts |
Count of login attempts |
Data Transfer MB |
Volume of data moved |
CPU Usage % |
Average CPU usage during session |
Memory Usage MB |
RAM usage in megabytes |
Threat Score |
Model-assigned risk score based on prior analysis |
Here’s an expanded and more detailed rewrite of your section on Cyber Attack Simulation:
9. Cyber Attack Simulation¶
As part of the next phase of the project, we will extend the platform to simulate a range of high-impact cyber attacks. These simulations will provide a dynamic testing environment to evaluate detection capabilities, assess organizational vulnerabilities, and enhance the system’s AI-powered threat response mechanisms. The simulated attack types will include:
- Phishing Attacks: Simulate social engineering campaigns to test user susceptibility to deceptive emails, credential harvesting, and fraudulent access attempts.
- Malware Attacks: Model the behavior and spread of malicious software such as keyloggers, spyware, trojans, and worms to assess endpoint defenses and containment strategies.
- Distributed Denial-of-Service (DDoS) Attacks: Emulate volumetric and application-layer attacks aimed at overwhelming network resources, disrupting services, and testing resilience under stress.
- Data Leak Attacks: Mimic unauthorized data exfiltration scenarios, both accidental and malicious, to evaluate monitoring, detection, and containment protocols.
- Insider Threats: Simulate misuse of access privileges by employees or contractors, focusing on the detection of anomalous behaviors within internal systems.
- Ransomware Attacks: Recreate file encryption and ransom demand scenarios to test system backups, alerting systems, and recovery processes.
Each simulation will be integrated into the platform’s AI analytics engine and risk dashboards, providing real-time threat scoring, response playbooks, and post-event analysis to support training, governance, and resilience planning.
Project Development¶
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Important libraries¶
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import pandas as pd
import numpy as np
import seaborn as sns
from datetime import datetime, timedelta
import random
import os # Import the os module to create directories
from google.colab import drive, files
drive.mount('/content/drive')
import gspread # for google sheets
from gspread_dataframe import set_with_dataframe # for google sheets
from google.auth.transport.requests import Request # for google sheets
from google.oauth2.service_account import Credentials # for google sheets
from imblearn.over_sampling import SMOTE
import tensorflow as tf
from tensorflow.keras import layers, models, Sequential
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping
from sklearn.ensemble import IsolationForest
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.svm import OneClassSVM
from sklearn.cluster import DBSCAN, KMeans
from sklearn.neighbors import LocalOutlierFactor
from sklearn.preprocessing import LabelEncoder, StandardScaler, MinMaxScaler
from sklearn.metrics import precision_score, recall_score, auc, average_precision_score, pairwise_distances
from sklearn.metrics import classification_report, accuracy_score, confusion_matrix, roc_curve
from sklearn.metrics import roc_auc_score, f1_score, precision_recall_curve, mean_squared_error
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
from sklearn.manifold import TSNE
import scipy.spatial
from matplotlib.ticker import FuncFormatter
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib import cm
from matplotlib.colors import Normalize
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import plotly.express as px
from fpdf import FPDF
from matplotlib.colors import LinearSegmentedColormap
from mpl_toolkits.mplot3d import Axes3D # Needed for 3D plotting
import pickle
import joblib
import shap
import umap
import warnings
import streamlit as st
warnings.filterwarnings("ignore")
Mounted at /content/drive
Data Preparation(Data synthetization & Preprocessing)¶
In this section we will generate a synthetic "realistic" dataset to reflect the real world user activities production data.
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# ----------------------Define parameters--------------------------------------
num_normal_issues = 800 # Normal samples
num_anomalous_issues = 200 # Anomalous samples#
total_issues = num_normal_issues + num_anomalous_issues
num_users = 100 # Number of unique users
num_reporters = 10 # Number of unique reporters
num_assignees = 20 # Number of unique assignees
num_departments = 5 # Number of unique departments
current_date = datetime.now()
start_date = datetime(2023, 1, 1)
end_date = datetime(current_date.year, current_date.month, current_date.day)
# --------------------------Define file paths--------------------------------
anomalous_data_file= "cybersecurity_dataset_for_google_drive_anomalous_data_v1.csv"
normal_data_file = "cybersecurity_dataset_for_google_drive_normal_data_v1.csv"
normal_and_anomalous_file = "cybersecurity_normal_and_anomalous_dataset_for_google_drive_v1.csv"
#Google drive
google_drive_data_folder = "/content/drive/My Drive/Cybersecurity Data"
google_drive_model_folder = "/content/drive/My Drive/Model deployment"
normal_data_file_path_to_google_drive = os.path.join(google_drive_data_folder, "cybersecurity_dataset_for_google_drive_normal_data_v1.csv")
anomalous_data_file_path_to_google_drive = os.path.join(google_drive_data_folder, "cybersecurity_dataset_for_google_drive_anomalous_data_v1.csv")
file_path_to_normal_and_anomalous_google_drive = os.path.join(google_drive_data_folder, "normal_and_anomalous_cybersecurity_dataset_for_google_drive_kb.csv")
key_threat_indicators_file_path_to_on_google_drive = os.path.join(google_drive_data_folder, "key_threat_indicators_df.csv")
scenarios_with_colors_file_path_to_on_google_drive = os.path.join(google_drive_data_folder, "scenarios_with_colors_df.csv")
resampled_file_path_to_google_drive = os.path.join(google_drive_data_folder, "cybersecurity_resampled_dataset_for_google_drive.csv")
model_deployment_path_to_google_drive = os.path.join(google_drive_model_folder)
Cybersecurity_Attack_report_data_google_drive = os.path.join(google_drive_data_folder, "Cybersecurity_Attack_Data_V0.csv")
Executive_Cybersecurity_Attack_Report_on_google_drive = os.path.join(google_drive_data_folder, "Executive_Cybersecurity_Attack_Report.pdf")
# ---------------------Generate normal issue metadata------------------------
issue_ids = [f"ISSUE-{i:04d}" for i in range(1, num_normal_issues + 1)]
issue_keys = [f"KEY-{i:04d}" for i in range(1, num_normal_issues + 1)]
KPI_list = [
"Network Security","Access Control","System Vulnerability",
"Penetration Testing Effectiveness","Management Oversight",
"Procurement Security", "Control Effectiveness",
"Asset Inventory Accuracy", "Vulnerability Remediation",
"Risk Management Maturity", "Risk Assessment Coverage"
]
KRI_list = [
"Data Breach", "Phishing Attack","Malware","Data Leak",
"Legal Compliance","Risk Exposure", "Cloud Security Posture",
"Unauthorized Access", "DDOS"
]
categories = KPI_list + KRI_list
severities = ["Low", "Medium", "High", "Critical"]
statuses = ["Open", "In Progress", "Resolved","Closed"]
reporters = [f"Reporter {i}" for i in range(1, num_reporters + 1)]
assignees = [f"Assignee {i}" for i in range(1, num_assignees + 1)]
users = [f"User_{i}" for i in range(1, num_users + 1)]
departments = ["IT", "Finance", "Operations", "HR", "Legal","Sales", "C-Suite Executives", "External Contractors"]
locations = ["CANADA", "USA", "Unknown", "EU", "DE", "FR", "JP", "CN", "AU", "IN", "UK"]
columns = [
"Issue ID", "Issue Key", "Issue Name", "Issue Volume", "Category", "Severity", "Status", "Reporters", "Assignees", "Date Reported", "Date Resolved", "Issue Response Time Days", "Impact Score", "Risk Level", "Department Affected", "Remediation Steps", "Cost", "KPI/KRI", "User ID","Timestamps", "Activity Type","User Location", "IP Location","Session Duration in Second", "Num Files Accessed", "Login Attempts", "Data Transfer MB", "CPU Usage %", "Memory Usage MB", "Threat Score", "Threat Level", "Defense Action"
]
#---------Datasets for documentation -------------------------------------------------------------------------
# Create the data for the DataFrame
#import pandas as pd
# Create the data for the DataFrame
ktis_data = {
"KIT": [
"Severity", "Impact Score", "Risk Level", "Response Time", "Category",
"Activity Type", "Login Attempts", "Num Files Accessed", "Data Transfer MB",
"CPU Usage %", "Memory Usage MB"
],
"Condition": [
"Critical = 10, High = 8, Medium = 5, Low = 2",
"1 to 10 (already a score)",
"High = 8, Medium = 5, Low = 2",
">7 days = 5, 3-7 days = 3, <3 days = 1",
"Unauthorized Access = 8, Phishing = 6, etc.",
"High-risk types (e.g., login, data_transfer)",
">5 = 5, 3-5 = 3, <3 = 1",
">10 = 5, 5-10 = 3, <5 = 1",
">100 MB = 5, 50-100 MB = 3, <50 MB = 1",
">80% = 5, 60-80% = 3, <60% = 1",
">8000 MB = 5, 4000-8000 MB = 3, <4000 MB = 1"
],
"Score": [
"2 - 10", "1 - 10", "2 - 8", "1 - 5", "1 - 8", "1 - 5", "1 - 5", "1 - 5", "1 - 5", "1 - 5", "1 - 5"
]
}
# Create the DataFrame
ktis_key_threat_indicators_df = pd.DataFrame(ktis_data)
# Create the data for the DataFrame scenarios with Colors
scenario_data = {
"Scenario": list(range(1, 17)),
"Threat Level": [
"Critical", "Critical", "Critical", "Critical",
"High", "High", "High", "High",
"Medium", "Medium", "Medium", "Medium",
"Low", "Low", "Low", "Low"
],
"Severity": [
"Critical", "High", "Medium", "Low",
"Critical", "High", "Medium", "Low",
"Critical", "High", "Medium", "Low",
"Critical", "High", "Medium", "Low"
],
"Suggested Color": [
"Dark Red", "Red", "Orange-Red", "Orange",
"Red", "Orange-Red", "Orange", "Yellow-Orange",
"Orange", "Yellow-Orange", "Yellow", "Light Yellow",
"Yellow", "Light Yellow", "Green-Yellow", "Green"
],
"Rationale": [
"Maximum urgency, both threat and impact are critical. Immediate action required.",
"Very high risk, threat is critical and impact is significant. Prioritize response.",
"Significant threat but moderate impact. Act promptly to prevent escalation.",
"High potential risk, current impact is minimal. Monitor closely and mitigate quickly.",
"High threat combined with critical impact. Needs immediate action.",
"High threat and significant impact. Prioritize response.",
"Elevated threat and moderate impact. Requires attention.",
"High threat with low impact. Proactive monitoring recommended.",
"Moderate threat with critical impact. Prioritize addressing the severity.",
"Medium threat with high impact. Needs resolution soon.",
"Medium threat and impact. Plan to address it.",
"Moderate threat, minimal impact. Monitor as needed.",
"Low threat but high impact. Address severity first.",
"Low threat with significant impact. Plan mitigation.",
"Low threat, moderate impact. Routine monitoring.",
"Minimal risk. No immediate action required."
]
}
# Create the DataFrame
scenarios_with_colors_df = pd.DataFrame(scenario_data)
#---------------------------------Define columns---------------------------------------
numerical_columns = [
"Timestamps", "Issue Response Time Days", "Impact Score", "Cost",
"Session Duration in Second", "Num Files Accessed", "Login Attempts",
"Data Transfer MB", "CPU Usage %", "Memory Usage MB", "Threat Score"
]
explanatory_data_analysis_columns = [
"Date Reported", "Issue Response Time Days", "Impact Score", "Cost",
"Session Duration in Second", "Num Files Accessed", "Login Attempts",
"Data Transfer MB", "CPU Usage %", "Memory Usage MB", "Threat Score"
]
user_activity_features = [
"Risk Level", "Issue Response Time Days", "Impact Score", "Cost",
"Session Duration in Second", "Num Files Accessed", "Login Attempts",
"Data Transfer MB", "CPU Usage %", "Memory Usage MB", "Threat Score"
]
initial_dates_columns = ["Date Reported", "Date Resolved", "Timestamps"]
categorical_columns = ["Issue ID", "Issue Key", "Issue Name", "Category", "Severity", "Status", "Reporters",
"Assignees", "Risk Level", "Department Affected", "Remediation Steps", "KPI/KRI",
"User ID", "Activity Type", "User Location", "IP Location", "Threat Level", "Defense Action", "Color"
]
features_engineering_columns = [
"Issue Response Time Days", "Impact Score", "Cost",
"Session Duration in Second", "Num Files Accessed", "Login Attempts",
"Data Transfer MB", "CPU Usage %", "Memory Usage MB", "Threat Score", "Threat Level"
]
numerical_behavioral_features = [
"Login Attempts", "Data Transfer MB", "CPU Usage %", "Memory Usage MB",
"Session Duration in Second", "Num Files Accessed", "Threat Score"
]
def get_column_dic():
columns_dic = {
"numerical_columns": [
"Timestamps", "Issue Response Time Days", "Impact Score", "Cost",
"Session Duration in Second", "Num Files Accessed", "Login Attempts",
"Data Transfer MB", "CPU Usage %", "Memory Usage MB", "Threat Score"
],
"explanatory_data_analysis_columns": [
"Date Reported", "Issue Response Time Days", "Impact Score", "Cost",
"Session Duration in Second", "Num Files Accessed", "Login Attempts",
"Data Transfer MB", "CPU Usage %", "Memory Usage MB", "Threat Score"
],
"user_activity_features": [
"Risk Level", "Issue Response Time Days", "Impact Score", "Cost",
"Session Duration in Second", "Num Files Accessed", "Login Attempts",
"Data Transfer MB", "CPU Usage %", "Memory Usage MB", "Threat Score"
],
"initial_dates_columns": ["Date Reported", "Date Resolved", "Timestamps"],
"categorical_columns": [
"Issue ID", "Issue Key", "Issue Name", "Category", "Severity", "Status", "Reporters",
"Assignees", "Risk Level", "Department Affected", "Remediation Steps", "KPI/KRI",
"User ID", "Activity Type", "User Location", "IP Location", "Threat Level", "Defense Action", "Color"
],
"features_engineering_columns": [
"Issue Response Time Days", "Impact Score", "Cost",
"Session Duration in Second", "Num Files Accessed", "Login Attempts",
"Data Transfer MB", "CPU Usage %", "Memory Usage MB", "Threat Score", "Threat Level"
],
"numerical_behavioral_features": [
"Login Attempts", "Data Transfer MB", "CPU Usage %", "Memory Usage MB",
"Session Duration in Second", "Num Files Accessed", "Threat Score"
]
}
return columns_dic
#for performance classes
#def get_performance_classes():
#level_mapping = {"Low": 0, "Medium": 1, "High": 2, "Critical": 3}
#class_names = list(level_mapping.keys())
# Define the colors
#colors = ["darkred", "red", "orangered", "orange", "yelloworange", "lightyellow", "yellow", "greenyellow", "green"]
colors = ["#8B0000", "#FF0000", "#FF4500", "#FFA500", "#FFB347", "#FFFFE0", "#FFFF00", "#ADFF2F", "#008000"]
# Create a colormap
custom_cmap = LinearSegmentedColormap.from_list("CustomCmap", colors)
def get_color_map():
# Define the colors
#colors = ["darkred", "red", "orangered", "orange", "yelloworange", "lightyellow", "yellow", "greenyellow", "green"]
colors = ["#8B0000", "#FF0000", "#FF4500", "#FFA500", "#FFB347", "#FFFFE0", "#FFFF00", "#ADFF2F", "#008000"]
# Create a colormap
custom_cmap = LinearSegmentedColormap.from_list("CustomCmap", colors)
return custom_cmap
#IP addresses, port numbers, packet sizes, and time intervals
# ---------------------Generate user activity metadata------------------------
activity_types = ["login", "file_access", "data_modification"]
# -----------------------------------------------------------------------
# Generate normal issue names for each KPI and KRI by Mapping
# normal issue name to issue category using a dictionary
# ---------------------------------------------------------------------
def generate_normal_issues_name(category):# Mapping issue name to issue category using a dictionary
issue_mapping = {
"Network Security": "Inadequate Firewall Configurations",
"Access Control": "Weak Authentication Protocols",
"System Vulnerability": "Outdated Operating System Components",
"Penetration Testing Effectiveness": "Unresolved Vulnerabilities from Latest Penetration Test",
"Management Oversight": "Inconsistent Review of Security Policies",
"Procurement Security": "Supplier Security Compliance Gaps",
"Control Effectiveness": "Insufficient Access Control Measures",
"Asset Inventory Accuracy": "Missing or Inaccurate Asset Records",
"Vulnerability Remediation": "Delayed Patching of Known Vulnerabilities",
"Risk Management Maturity": "Incomplete Risk Management Framework",
"Risk Assessment Coverage": "Insufficient Coverage in Annual Risk Assessment",
"Data Breach": "Unauthorized Access Leading to Data Exposure",
"Phishing Attack": "Successful Phishing Attempt Targeting Executives",
"Malware": "Detected Malware Infiltration in Internal Systems",
"Data Leak": "Sensitive Data Leak via Misconfigured Cloud Storage",
"Legal Compliance": "Non-Compliance with Data Protection Regulations",
"Risk Exposure": "Increased Exposure due to Insufficient Data Encryption",
"Cloud Security Posture": "Weak Cloud Storage Access Controls",
"Unauthorized Access": "Access by Unauthorized Personnel Detected",
"DDOS": "High-Volume Distributed Denial-of-Service Attack"
}
return issue_mapping.get(category, "Unknown Issue")
#-------------------------Generate anomalous issues metadata---------------------------------------------
anomalous_issue_ids = [f"ISSUE-{i:04d}" for i in range(num_anomalous_issues +1, total_issues + 1)]
anomalous_issue_keys = [f"KEY-{i:04d}" for i in range(num_anomalous_issues +1, total_issues + 1)]
# -------------------------------------------------------------------
# Generate anomalous issue names for each KPI and KRI by Mapping
# anomalous issue name to issue category using a dictionary
# ------------------------------------------------------------------
def generate_anomalous_issue_name(category):
anomalous_issue_mapping = {
"Network Security": "Sudden Increase in Unfiltered Traffic",
"Access Control": "Multiple Unauthorized Access Attempts Detected",
"System Vulnerability": "Newly Discovered Vulnerabilities in Core Systems",
"Penetration Testing Effectiveness": "Critical Issues Not Detected in Last Penetration Test",
"Management Oversight": "High Frequency of Policy Violations",
"Procurement Security": "Supplier Network Breach Exposure",
"Control Effectiveness": "Ineffective Access Controls in High-Sensitivity Areas",
"Asset Inventory Accuracy": "Significant Number of Untracked Devices",
"Vulnerability Remediation": "Delayed Patching of Critical Vulnerabilities",
"Risk Management Maturity": "Lack of Updated Risk Management Procedures",
"Risk Assessment Coverage": "Unassessed High-Risk Areas",
"Data Breach": "Unusual Data Transfer Volumes Detected",
"Phishing Attack": "Targeted Phishing Campaign Against Executives",
"Malware": "Malware Detected in Core System Components",
"Data Leak": "Unusual Data Access from External Locations",
"Legal Compliance": "Potential Non-Compliance Detected in Sensitive Data Handling",
"Risk Exposure": "Unanticipated Increase in Risk Exposure",
"Cloud Security Posture": "Weak Access Controls on Critical Cloud Resources",
"Unauthorized Access": "Spike in Unauthorized Access Attempts",
"DDOS": "High-Volume Distributed Denial-of-Service Attack from Multiple Sources"
}
return anomalous_issue_mapping.get(category, "Unknown Issue")
#-------------------------Implementation-----------------------------------
# filter KPI Vs KRI
def filter_kpi_and_kri(category, KPI_list, KI_list):
if category in KPI_list:
return 'KPI'
else:
return 'KRI'
def generate_cpu_memory_usage(threat_level):
"""
Generate synthetic CPU usage % and Memory usage MB based on threat level.
"""
if threat_level == "Low":
cpu = np.random.normal(loc=30, scale=5)
mem = np.random.normal(loc=2000, scale=400)
elif threat_level == "Medium":
cpu = np.random.normal(loc=55, scale=10)
mem = np.random.normal(loc=5000, scale=800)
elif threat_level == "High":
cpu = np.random.normal(loc=75, scale=12)
mem = np.random.normal(loc=8000, scale=1000)
elif threat_level == "Critical":
cpu = np.random.normal(loc=90, scale=5)
mem = np.random.normal(loc=12000, scale=1200)
else:
cpu = np.random.normal(loc=50, scale=15)
mem = np.random.normal(loc=4000, scale=1000)
return max(0, min(cpu, 100)), max(512, mem) # Clamp CPU to [0,100] and Memory min 512MB
#Generate normal volatility
def generate_normal_volatile_data(severity, base_value, volatility=0.3):
return round(base_value + base_value * volatility * (np.random.randn())* (1.2 if severity in ['High', 'Critical'] else 1), 2)
def generate_normal_volatile_access_controle(severity, base_value, volatility=0.3):
return round(base_value + base_value * volatility * int(np.random.poisson(lam=5))* (1.2 if severity in ['High', 'Critical'] else 1), 2)
def generate_normal_volatile_login_attempts(severity, base_value, volatility=0.3):
return round(base_value + base_value * volatility * int(np.random.poisson(lam=3))* (1.2 if severity in ['High', 'Critical'] else 1), 2)
def generate_normal_volatile_data_transfer(severity, base_value, volatility=0.3):
return round(base_value + base_value * volatility * round(np.random.exponential(scale=10),2)* (1.2 if severity in ['High', 'Critical'] else 1), 2)
def generate_normal_cost_volatile(severity, base_value, volatility=0.3):
return round(base_value + base_value * volatility * round(np.random.uniform(500, 10000),2)* (1.2 if severity in ['High', 'Critical'] else 1), 2)
def generate_normal_timestamp_volatile(date_reported):
return date_reported + timedelta(hours=random.randint(0, 23), minutes=random.randint(0, 59))
#Generate anomalous volatility to inject more noise
def generate_anomalous_volatile_data(severity, base_value, volatility=0.3):
return round(base_value + base_value * volatility * (np.random.randn())* (1.2 if severity in ['High', 'Critical'] else 1), 2)
def generate_anomalous_volatile_access_controle(severity, base_value, volatility=0.3):
return round(base_value + base_value * volatility * int(np.random.poisson(lam=5))* (1.2 if severity in ['High', 'Critical'] else 1), 2)
def generate_anomalous_volatile_login_attempts(severity, base_value, volatility=0.3):
return round(base_value + base_value * volatility * int(np.random.poisson(lam=3))* (1.2 if severity in ['High', 'Critical'] else 1), 2)
def generate_anomalous_volatile_data_transfer(severity, base_value, volatility=0.3):
return round(base_value + base_value * volatility * round(np.random.exponential(scale=10),2)* (1.2 if severity in ['High', 'Critical'] else 1), 2)
def generate_anomalous_cost_volatile(severity, base_value, volatility=0.3):
return round(base_value + base_value * volatility * round(np.random.uniform(500, 10000),2)* (1.2 if severity in ['High', 'Critical'] else 1), 2)
def generate_anomalous_timestamp_volatile(severity, date_reported, volatility=0.3):
return date_reported + timedelta(hours=random.randint(0, 23), minutes=random.randint(0, 59))*volatility * (1.2 if severity in ['High', 'Critical'] else 1)
# Function to generate a random start date within a specific date range--
def random_date(start, end):
return start + timedelta(days=np.random.randint(0, (end - start).days))
# ----------------------------------Define threat level calculation-------------------------------------------------------
def calculate_threat_level(severity, impact_score, risk_level, response_time_days,
login_attempts, num_files_accessed, data_transfer_MB,
cpu_usage_percent, memory_usage_MB):
# Define scores based on input criteria
severity_score = {"Critical": 10, "High": 8, "Medium": 5, "Low": 2}.get(severity, 1)
risk_score = {"Critical": 10, "High": 8, "Medium": 5, "Low": 2}.get(risk_level, 1)
response_time_score = 5 if response_time_days > 7 else 3 if response_time_days > 3 else 1
login_attempts_score = 5 if login_attempts > 5 else 3 if login_attempts > 3 else 1
files_accessed_score = 5 if num_files_accessed > 10 else 3 if num_files_accessed > 5 else 1
data_transfer_score = 5 if data_transfer_MB > 100 else 3 if data_transfer_MB > 50 else 1
# New metrics: CPU usage and memory usage
cpu_usage_score = 5 if cpu_usage_percent > 85 else 3 if cpu_usage_percent > 60 else 1
memory_usage_score = 5 if memory_usage_MB > 10000 else 3 if memory_usage_MB > 6000 else 1
# Aggregate the scores
threat_score = (
0.25 * severity_score +
0.2 * impact_score +
0.15 * risk_score +
0.1 * response_time_score +
0.05 * login_attempts_score +
0.05 * files_accessed_score +
0.05 * data_transfer_score +
0.075 * cpu_usage_score +
0.075 * memory_usage_score
)
# Determine threat level based on the calculated score
if threat_score >= 9:
return "Critical", threat_score
elif threat_score >= 7:
return "High", threat_score
elif threat_score >= 4:
return "Medium", threat_score
else:
return "Low", threat_score
##for performance classes
#def get_performance_classes():
level_mapping = {"Low": 0, "Medium": 1, "High": 2, "Critical": 3}
class_names = list(level_mapping.keys())
#------------------------ Adaptive defense mechanism based on threat level and conditions----------------------------------
def adaptive_defense_mechanism(row):
"""
Determines the adaptive response based on threat level, severity, and activity context.
"""
action = "Monitor"
# Map the threat level and severity to actions based on scenarios
threat_severity_actions = {
("Critical", "Critical"): "Immediate System-wide Shutdown & Investigation",
("Critical", "High"): "Escalate to Security Operations Center (SOC) & Block User",
("Critical", "Medium"): "Isolate Affected System & Restrict User Access",
("Critical", "Low"): "Increase Monitoring & Schedule Review",
("High", "Critical"): "Escalate to SOC & Restrict Critical System Access",
("High", "High"): "Restrict User Activity & Monitor Logs",
("High", "Medium"): "Alert Security Team & Review Logs",
("High", "Low"): "Flag for Review",
("Medium", "Critical"): "Increase Monitoring & Investigate",
("Medium", "High"): "Schedule Investigation",
("Medium", "Medium"): "Routine Monitoring",
("Medium", "Low"): "Log Activity for Reference",
("Low", "Critical"): "Log and Notify",
("Low", "High"): "Routine Monitoring",
("Low", "Medium"): "Log for Reference",
("Low", "Low"): "No Action Needed"
}
# Assign action based on scenario
action = threat_severity_actions.get((row["Threat Level"], row["Severity"]), action)
# Additional responses based on user behavior and thresholds
if row["Threat Level"] in ["Critical", "High"] and row["Login Attempts"] > 5:
action += " | Lock Account & Alert"
if row["Activity Type"] == "File Access" and row["Num Files Accessed"] > 15:
action += " | Restrict File Access"
if row["Activity Type"] == "Login" and row["Login Attempts"] > 10:
action += " | Require Multi-Factor Authentication (MFA)"
if row["Data Transfer MB"] > 100:
action += " | Limit Data Transfer"
return action
#-----------------------------------------------------------------------
def generate_normal_issues_df(p_issue_ids, p_issue_keys):
normal_issues_data = []
for issue_id, issue_key in zip(p_issue_ids, p_issue_keys):
issue_volume = 1
category = random.choice(categories)
issue_name = generate_normal_issues_name(category)
severity = random.choice(severities)
status = random.choice(statuses)
reporter = random.choice(reporters)
assignee = random.choice(assignees)
date_reported = random_date(start_date, end_date)
date_resolved = date_reported + timedelta(days=random.randint(1, 10)) if status in ["Resolved", "Closed"] else current_date
issue_response_time_days = (date_resolved - date_reported).days
impact_score = max(2, generate_normal_volatile_data(severity, base_value=50, volatility=0.5))
risk_level = 'Critical' if impact_score > 10 else 'High' if impact_score > 7 else 'Medium' if impact_score > 4 else 'Low'
department_affected = random.choice(departments)
remediation_steps = f"Steps to resolve {issue_name}"
cost = max(600, generate_normal_cost_volatile(severity, base_value=500, volatility=0.5))
kpi_kri = filter_kpi_and_kri(category, KPI_list, KRI_list)
user_location = random.choice(locations)
user_id = random.choice(users)
timestamp = date_reported + timedelta(hours=np.random.randint(0, 24), minutes=np.random.randint(0, 60))
activity_type = random.choice(activity_types)
ip_location = user_location if np.random.rand() > 0.2 else random.choice([loc for loc in locations if loc != user_location])
session_duration = max(900, int(generate_normal_volatile_data(severity, base_value=1000, volatility=0.7)))
num_files_accessed = max(26, int(generate_normal_volatile_access_controle(severity, base_value=3, volatility=1.0)))
login_attempts = max(1, int(generate_normal_volatile_login_attempts(severity, base_value=3, volatility=1.0)))
data_transfer_MB = max(1, generate_normal_volatile_data_transfer(severity, base_value=500, volatility=0.5))
# New metrics
cpu_usage_percent = random.uniform(20, 80)
memory_usage_MB = random.randint(3000, 8000)
threat_level, threat_score = calculate_threat_level(
severity, impact_score, risk_level, issue_response_time_days,
login_attempts, num_files_accessed, data_transfer_MB,
cpu_usage_percent, memory_usage_MB
)
row = {
"Severity": severity, "Impact Score": impact_score, "Risk Level": risk_level,
"Issue Response Time Days": issue_response_time_days, "Login Attempts": login_attempts,
"Num Files Accessed": num_files_accessed, "Data Transfer MB": data_transfer_MB,
"CPU Usage %": cpu_usage_percent, "Memory Usage MB": memory_usage_MB,
"Threat Level": threat_level, "Activity Type": activity_type
}
defense_action = adaptive_defense_mechanism(row)
normal_issues_data.append([
issue_id, issue_key, issue_name, issue_volume, category, severity, status, reporter, assignee,
date_reported, date_resolved, issue_response_time_days, impact_score, risk_level, department_affected,
remediation_steps, cost, kpi_kri, user_id, timestamp, activity_type, user_location, ip_location,
session_duration, num_files_accessed, login_attempts, data_transfer_MB,
cpu_usage_percent, memory_usage_MB, threat_score, threat_level, defense_action
])
df = pd.DataFrame(normal_issues_data, columns=columns)
return df
# Create anomalous issues dataset
def generate_anomalous_issues_df(p_anomalous_issue_ids, p_anomalous_issue_keys):
anomalous_normal_issues_data = []
for issue_id, issue_key in zip(p_anomalous_issue_ids, p_anomalous_issue_keys):
issue_volume = 1
category = random.choice(categories)
issue_name = generate_anomalous_issue_name(category)
severity = np.random.choice(severities, p=[0.1, 0.2, 0.4, 0.3])
status = random.choice(statuses)
reporter = random.choice(reporters)
assignee = random.choice(assignees)
date_reported = random_date(start_date, end_date)
date_resolved = date_reported + timedelta(days=random.randint(1, 10)) if status in ["Resolved", "Closed"] else current_date
issue_response_time_days = (date_resolved - date_reported).days
impact_score = max(5, generate_anomalous_volatile_data(severity, base_value=100, volatility=0.65))
risk_level = 'Critical' if impact_score > 10 else 'High' if impact_score > 7 else 'Medium' if impact_score > 4 else 'Low'
department_affected = random.choice(departments)
remediation_steps = f"Steps to resolve {issue_name}"
cost = max(1000, generate_anomalous_cost_volatile(severity, base_value=1000, volatility=0.5))
kpi_kri = filter_kpi_and_kri(category, KPI_list, KRI_list)
user_location = random.choice(locations)
user_id = random.choice(users)
timestamp = date_reported + timedelta(hours=np.random.randint(0, 24), minutes=np.random.randint(0, 60))
activity_type = random.choice(activity_types)
ip_location = user_location if np.random.rand() < 0.2 else random.choice([loc for loc in locations if loc != user_location])
session_duration = max(10, int(generate_anomalous_volatile_data(severity, base_value=1800, volatility=0.85)))
num_files_accessed = max(10, int(generate_anomalous_volatile_access_controle(severity, base_value=100, volatility=1.0)))
login_attempts = max(10, int(generate_anomalous_volatile_login_attempts(severity, base_value=30, volatility=1.0)))
data_transfer_MB = max(10, generate_anomalous_volatile_data_transfer(severity, base_value=5000, volatility=0.85))
# New metrics
cpu_usage_percent = random.uniform(85, 100)
memory_usage_MB = random.randint(9000, 13000)
threat_level, threat_score = calculate_threat_level(
severity, impact_score, risk_level, issue_response_time_days,
login_attempts, num_files_accessed, data_transfer_MB,
cpu_usage_percent, memory_usage_MB
)
row = {
'Severity': severity, 'Impact Score': impact_score, 'Risk Level': risk_level,
'Issue Response Time Days': issue_response_time_days, 'Login Attempts': login_attempts,
'Num Files Accessed': num_files_accessed, 'Data Transfer MB': data_transfer_MB,
'CPU Usage %': cpu_usage_percent, 'Memory Usage MB': memory_usage_MB,
'Threat Level': threat_level, 'Activity Type': activity_type
}
defense_action = adaptive_defense_mechanism(row)
anomalous_normal_issues_data.append([
issue_id, issue_key, issue_name, issue_volume, category, severity, status, reporter, assignee,
date_reported, date_resolved, issue_response_time_days, impact_score, risk_level, department_affected,
remediation_steps, cost, kpi_kri, user_id, timestamp, activity_type, user_location, ip_location,
session_duration, num_files_accessed, login_attempts, data_transfer_MB,
cpu_usage_percent, memory_usage_MB, threat_score, threat_level, defense_action
])
df = pd.DataFrame(anomalous_normal_issues_data, columns=columns)
return df
#------------------------------Matching Treat to coloor--------------------------------------------------
# Define color coding function
def map_threat_severity_to_color(df):
def assign_color(threat, severity):
if threat == "Critical":
if severity == "Critical":
return "Dark Red"
elif severity == "High":
return "Red"
elif severity == "Medium":
return "Orange-Red"
else:
return "Orange"
elif threat == "High":
if severity == "Critical":
return "Red"
elif severity == "High":
return "Orange-Red"
elif severity == "Medium":
return "Orange"
else:
return "Yellow-Orange"
elif threat == "Medium":
if severity == "Critical":
return "Orange"
elif severity == "High":
return "Yellow-Orange"
elif severity == "Medium":
return "Yellow"
else:
return "Light Yellow"
else: # Low threat
if severity == "Critical":
return "Yellow"
elif severity == "High":
return "Light Yellow"
elif severity == "Medium":
return "Green-Yellow"
else:
return "Green"
# Assign colors
df["Color"] = df.apply(lambda row: assign_color(row["Threat Level"], row["Severity"]), axis=1)
return df
#------------------------------------Save the DataFrame to a CSV file--------------------------------------------------
def save_dataframe_to_google_drive(df, save_path):
# Ensure the directory exists
directory = os.path.dirname(save_path)
if not os.path.exists(directory):
os.makedirs(directory)
df.to_csv(save_path, index=False)
print(f"DataFrame saved to: {save_path}")
def data_generation_pipeline(p_issue_ids, p_issue_keys, p_anomalous_issue_ids, p_anomalous_issue_keys):
# --------------Combine normal and anomalous data-------------------------------
normal_issues_df = generate_normal_issues_df(p_issue_ids, p_issue_keys)
anomalous_issues_df = generate_normal_issues_df(p_anomalous_issue_ids, p_anomalous_issue_keys)
normal_and_anomalous_df = pd.concat([normal_issues_df, anomalous_issues_df], ignore_index=True)
#Define security defense actions
normal_and_anomalous_df = map_threat_severity_to_color(normal_and_anomalous_df)
# remove the normal_and_anomalous_df rows corresponding to Null / NaN, "Unknown" or "Undefined" rows in the ["Threat Level"], ["Risk Level"] and ["Severity"]columns
normal_and_anomalous_df = normal_and_anomalous_df.dropna(subset=["Severity"])
normal_and_anomalous_df = normal_and_anomalous_df[normal_and_anomalous_df["Severity"] != "Undefined"]
normal_and_anomalous_df = normal_and_anomalous_df[normal_and_anomalous_df["Severity"] != "Unknown"]
normal_and_anomalous_df = normal_and_anomalous_df.dropna(subset=["Risk Level"])
normal_and_anomalous_df = normal_and_anomalous_df[normal_and_anomalous_df["Risk Level"] != "Undefined"]
normal_and_anomalous_df = normal_and_anomalous_df[normal_and_anomalous_df["Risk Level"] != "Unknown"]
normal_and_anomalous_df = normal_and_anomalous_df.dropna(subset=["Threat Level"])
normal_and_anomalous_df = normal_and_anomalous_df[normal_and_anomalous_df["Threat Level"] != "Unknown"]
normal_and_anomalous_df = normal_and_anomalous_df[normal_and_anomalous_df["Threat Level"] != "Undefined"]
return normal_issues_df, anomalous_issues_df, normal_and_anomalous_df
# -------------------------backup the data files-------------------------------
#Save the data to CSV to google drive
def save_the_data_to_CSV_to_google_drive(p_normal_issues_df, p_anomalous_issues_df, p_normal_and_anomalous_df,
p_ktis_key_threat_indicators_df, p_scenarios_with_colors_df):
save_dataframe_to_google_drive(p_normal_issues_df, normal_data_file_path_to_google_drive)
save_dataframe_to_google_drive(p_anomalous_issues_df, anomalous_data_file_path_to_google_drive)
save_dataframe_to_google_drive(p_normal_and_anomalous_df, file_path_to_normal_and_anomalous_google_drive)
#---
save_dataframe_to_google_drive(p_ktis_key_threat_indicators_df, key_threat_indicators_file_path_to_on_google_drive)
save_dataframe_to_google_drive(p_scenarios_with_colors_df, scenarios_with_colors_file_path_to_on_google_drive)
# -------------------------Display the data frames-----------------------------
def display_the_data_frames(p_normal_issues_df, p_anomalous_issues_df, p_normal_and_anomalous_df,
p_ktis_key_threat_indicators_df, p_scenarios_with_colors_df):
display(p_normal_issues_df.info())
print('\nData statics summary\n')
display(p_normal_issues_df.describe().transpose())
print('\nNormal_issues_df \n')
display(p_normal_issues_df.head())
print('\nanomalous_issues_df Data structure\n')
display(p_anomalous_issues_df.info())
print('\nanomalous_issues_df Data statics summary\n')
display(p_anomalous_issues_df.describe().transpose())
print('\nAnomalous_issues_df \n')
display(p_anomalous_issues_df.head())
print('\nNormal & anomalous combined Data structure\n')
display(p_normal_and_anomalous_df.info())
print('\nData statics summary\n')
display(p_normal_and_anomalous_df.describe().transpose())
print('\nNormal & anomalous combined Data\n')
display(p_normal_and_anomalous_df.head())
print('\n')
print('\nKey threat indicators Data structure\n')
display(p_ktis_key_threat_indicators_df)
print('\nScenarios with colors Data structure\n')
display(p_scenarios_with_colors_df)
print('\n')
#--------------------------------------------------data_preparation_pipeline-----------------------------------------------
normal_issues_df, anomalous_issues_df, real_world_simulated_normal_and_anomalous_df = data_generation_pipeline(issue_ids,
issue_keys,
anomalous_issue_ids,
anomalous_issue_keys)
#-------------------------
save_dataframe_to_google_drive(normal_issues_df, normal_data_file_path_to_google_drive)
save_dataframe_to_google_drive(anomalous_issues_df, anomalous_data_file_path_to_google_drive)
save_dataframe_to_google_drive(real_world_simulated_normal_and_anomalous_df,
file_path_to_normal_and_anomalous_google_drive)
#---
save_dataframe_to_google_drive(ktis_key_threat_indicators_df, key_threat_indicators_file_path_to_on_google_drive)
save_dataframe_to_google_drive(scenarios_with_colors_df, scenarios_with_colors_file_path_to_on_google_drive)
#---------------------
display_the_data_frames(normal_issues_df, anomalous_issues_df,
real_world_simulated_normal_and_anomalous_df ,
ktis_key_threat_indicators_df,
scenarios_with_colors_df)
#print(display)
DataFrame saved to: /content/drive/My Drive/Cybersecurity Data/cybersecurity_dataset_for_google_drive_normal_data_v1.csv DataFrame saved to: /content/drive/My Drive/Cybersecurity Data/cybersecurity_dataset_for_google_drive_anomalous_data_v1.csv DataFrame saved to: /content/drive/My Drive/Cybersecurity Data/normal_and_anomalous_cybersecurity_dataset_for_google_drive_kb.csv DataFrame saved to: /content/drive/My Drive/Cybersecurity Data/key_threat_indicators_df.csv DataFrame saved to: /content/drive/My Drive/Cybersecurity Data/scenarios_with_colors_df.csv <class 'pandas.core.frame.DataFrame'> RangeIndex: 800 entries, 0 to 799 Data columns (total 32 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Issue ID 800 non-null object 1 Issue Key 800 non-null object 2 Issue Name 800 non-null object 3 Issue Volume 800 non-null int64 4 Category 800 non-null object 5 Severity 800 non-null object 6 Status 800 non-null object 7 Reporters 800 non-null object 8 Assignees 800 non-null object 9 Date Reported 800 non-null datetime64[ns] 10 Date Resolved 800 non-null datetime64[ns] 11 Issue Response Time Days 800 non-null int64 12 Impact Score 800 non-null float64 13 Risk Level 800 non-null object 14 Department Affected 800 non-null object 15 Remediation Steps 800 non-null object 16 Cost 800 non-null float64 17 KPI/KRI 800 non-null object 18 User ID 800 non-null object 19 Timestamps 800 non-null datetime64[ns] 20 Activity Type 800 non-null object 21 User Location 800 non-null object 22 IP Location 800 non-null object 23 Session Duration in Second 800 non-null int64 24 Num Files Accessed 800 non-null int64 25 Login Attempts 800 non-null int64 26 Data Transfer MB 800 non-null float64 27 CPU Usage % 800 non-null float64 28 Memory Usage MB 800 non-null int64 29 Threat Score 800 non-null float64 30 Threat Level 800 non-null object 31 Defense Action 800 non-null object dtypes: datetime64[ns](3), float64(5), int64(6), object(18) memory usage: 200.1+ KB
None
Data statics summary
| count | mean | min | 25% | 50% | 75% | max | std | |
|---|---|---|---|---|---|---|---|---|
| Issue Volume | 800.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 |
| Date Reported | 800 | 2024-04-27 23:33:00 | 2023-01-01 00:00:00 | 2023-08-25 00:00:00 | 2024-04-23 00:00:00 | 2025-01-04 12:00:00 | 2025-09-15 00:00:00 | NaN |
| Date Resolved | 800 | 2025-01-12 12:55:18.961600512 | 2023-01-05 00:00:00 | 2024-04-15 00:00:00 | 2025-09-19 22:15:24.985807872 | 2025-09-19 22:15:24.985807872 | 2025-09-24 00:00:00 | NaN |
| Issue Response Time Days | 800.0 | 259.09 | 1.0 | 6.0 | 14.5 | 507.5 | 992.0 | 328.911234 |
| Impact Score | 800.0 | 50.043288 | 2.0 | 31.605 | 48.735 | 67.51 | 139.92 | 26.377728 |
| Cost | 800.0 | 1469567.359375 | 126027.5 | 816350.625 | 1480199.0 | 2067930.0 | 2979902.0 | 757918.224268 |
| Timestamps | 800 | 2024-04-28 11:04:45.150000128 | 2023-01-01 02:34:00 | 2023-08-25 09:34:45 | 2024-04-23 06:12:00 | 2025-01-05 06:31:30 | 2025-09-15 04:44:00 | NaN |
| Session Duration in Second | 800.0 | 1268.8075 | 900.0 | 900.0 | 992.5 | 1547.25 | 3314.0 | 499.574267 |
| Num Files Accessed | 800.0 | 26.94875 | 26.0 | 26.0 | 26.0 | 26.0 | 42.0 | 2.608568 |
| Login Attempts | 800.0 | 12.83 | 3.0 | 9.0 | 12.0 | 17.0 | 35.0 | 5.751082 |
| Data Transfer MB | 800.0 | 3328.455625 | 500.0 | 1312.25 | 2489.5 | 4283.5 | 18443.0 | 2858.860297 |
| CPU Usage % | 800.0 | 49.752375 | 20.012005 | 35.353717 | 49.387609 | 63.994126 | 79.975415 | 17.241021 |
| Memory Usage MB | 800.0 | 5528.99875 | 3004.0 | 4339.25 | 5528.5 | 6757.25 | 7995.0 | 1420.330472 |
| Threat Score | 800.0 | 14.390095 | 2.5 | 10.756 | 14.124 | 18.0065 | 33.684 | 5.529568 |
Normal_issues_df
| Issue ID | Issue Key | Issue Name | Issue Volume | Category | Severity | Status | Reporters | Assignees | Date Reported | ... | IP Location | Session Duration in Second | Num Files Accessed | Login Attempts | Data Transfer MB | CPU Usage % | Memory Usage MB | Threat Score | Threat Level | Defense Action | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ISSUE-0001 | KEY-0001 | Unauthorized Access Leading to Data Exposure | 1 | Data Breach | Low | Closed | Reporter 7 | Assignee 16 | 2023-12-07 | ... | JP | 1002 | 26 | 6 | 3420.0 | 34.417556 | 7717 | 9.682 | Critical | Increase Monitoring & Schedule Review | Lock A... |
| 1 | ISSUE-0002 | KEY-0002 | Increased Exposure due to Insufficient Data En... | 1 | Risk Exposure | Low | In Progress | Reporter 1 | Assignee 4 | 2023-05-05 | ... | AU | 1649 | 26 | 9 | 2825.0 | 38.368115 | 7828 | 14.314 | Critical | Increase Monitoring & Schedule Review | Lock A... |
| 2 | ISSUE-0003 | KEY-0003 | Non-Compliance with Data Protection Regulations | 1 | Legal Compliance | Medium | Closed | Reporter 3 | Assignee 6 | 2024-05-03 | ... | AU | 2190 | 26 | 6 | 1022.5 | 21.429354 | 4263 | 18.496 | Critical | Isolate Affected System & Restrict User Access... |
| 3 | ISSUE-0004 | KEY-0004 | Insufficient Coverage in Annual Risk Assessment | 1 | Risk Assessment Coverage | Low | Resolved | Reporter 3 | Assignee 17 | 2025-06-22 | ... | USA | 907 | 36 | 18 | 2692.5 | 33.896298 | 6366 | 15.352 | Critical | Increase Monitoring & Schedule Review | Lock A... |
| 4 | ISSUE-0005 | KEY-0005 | Inconsistent Review of Security Policies | 1 | Management Oversight | High | In Progress | Reporter 7 | Assignee 13 | 2024-03-28 | ... | DE | 900 | 42 | 3 | 3122.0 | 53.059948 | 5927 | 18.902 | Critical | Escalate to Security Operations Center (SOC) &... |
5 rows × 32 columns
anomalous_issues_df Data structure <class 'pandas.core.frame.DataFrame'> RangeIndex: 800 entries, 0 to 799 Data columns (total 32 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Issue ID 800 non-null object 1 Issue Key 800 non-null object 2 Issue Name 800 non-null object 3 Issue Volume 800 non-null int64 4 Category 800 non-null object 5 Severity 800 non-null object 6 Status 800 non-null object 7 Reporters 800 non-null object 8 Assignees 800 non-null object 9 Date Reported 800 non-null datetime64[ns] 10 Date Resolved 800 non-null datetime64[ns] 11 Issue Response Time Days 800 non-null int64 12 Impact Score 800 non-null float64 13 Risk Level 800 non-null object 14 Department Affected 800 non-null object 15 Remediation Steps 800 non-null object 16 Cost 800 non-null float64 17 KPI/KRI 800 non-null object 18 User ID 800 non-null object 19 Timestamps 800 non-null datetime64[ns] 20 Activity Type 800 non-null object 21 User Location 800 non-null object 22 IP Location 800 non-null object 23 Session Duration in Second 800 non-null int64 24 Num Files Accessed 800 non-null int64 25 Login Attempts 800 non-null int64 26 Data Transfer MB 800 non-null float64 27 CPU Usage % 800 non-null float64 28 Memory Usage MB 800 non-null int64 29 Threat Score 800 non-null float64 30 Threat Level 800 non-null object 31 Defense Action 800 non-null object dtypes: datetime64[ns](3), float64(5), int64(6), object(18) memory usage: 200.1+ KB
None
anomalous_issues_df Data statics summary
| count | mean | min | 25% | 50% | 75% | max | std | |
|---|---|---|---|---|---|---|---|---|
| Issue Volume | 800.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 |
| Date Reported | 800 | 2024-06-04 13:46:12 | 2023-01-01 00:00:00 | 2023-10-11 12:00:00 | 2024-06-24 12:00:00 | 2025-01-21 06:00:00 | 2025-09-18 00:00:00 | NaN |
| Date Resolved | 800 | 2025-01-11 19:57:35.524490752 | 2023-01-03 00:00:00 | 2024-06-09 00:00:00 | 2025-07-26 12:00:00 | 2025-09-19 22:15:24.985807872 | 2025-09-23 00:00:00 | NaN |
| Issue Response Time Days | 800.0 | 220.81625 | 1.0 | 6.0 | 10.0 | 415.75 | 982.0 | 298.328959 |
| Impact Score | 800.0 | 50.998988 | 2.0 | 31.53 | 51.055 | 69.6375 | 130.08 | 27.280305 |
| Cost | 800.0 | 1480320.7575 | 131287.5 | 753371.25 | 1506133.25 | 2135316.5 | 2982839.0 | 792929.473049 |
| Timestamps | 800 | 2024-06-05 01:43:37.650000128 | 2023-01-01 12:16:00 | 2023-10-12 00:55:45 | 2024-06-24 15:31:00 | 2025-01-22 02:07:00 | 2025-09-18 05:48:00 | NaN |
| Session Duration in Second | 800.0 | 1284.37875 | 900.0 | 900.0 | 1018.0 | 1536.0 | 3227.0 | 514.051253 |
| Num Files Accessed | 800.0 | 26.91 | 26.0 | 26.0 | 26.0 | 26.0 | 46.0 | 2.527327 |
| Login Attempts | 800.0 | 12.47875 | 3.0 | 9.0 | 12.0 | 17.0 | 35.0 | 5.779819 |
| Data Transfer MB | 800.0 | 3219.165625 | 502.5 | 1318.25 | 2355.0 | 4311.5 | 15955.0 | 2653.307045 |
| CPU Usage % | 800.0 | 49.446818 | 20.044128 | 35.089856 | 47.628354 | 64.971438 | 79.892365 | 17.259708 |
| Memory Usage MB | 800.0 | 5421.9775 | 3003.0 | 4203.25 | 5335.0 | 6651.75 | 7991.0 | 1425.180775 |
| Threat Score | 800.0 | 14.58611 | 2.5 | 10.8045 | 14.546 | 18.2365 | 31.066 | 5.649825 |
Anomalous_issues_df
| Issue ID | Issue Key | Issue Name | Issue Volume | Category | Severity | Status | Reporters | Assignees | Date Reported | ... | IP Location | Session Duration in Second | Num Files Accessed | Login Attempts | Data Transfer MB | CPU Usage % | Memory Usage MB | Threat Score | Threat Level | Defense Action | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ISSUE-0201 | KEY-0201 | Missing or Inaccurate Asset Records | 1 | Asset Inventory Accuracy | Low | Closed | Reporter 10 | Assignee 10 | 2025-07-22 | ... | USA | 1420 | 26 | 30 | 612.5 | 21.837212 | 5156 | 19.392 | Critical | Increase Monitoring & Schedule Review | Lock A... |
| 1 | ISSUE-0202 | KEY-0202 | Incomplete Risk Management Framework | 1 | Risk Management Maturity | Low | In Progress | Reporter 2 | Assignee 10 | 2024-11-07 | ... | UK | 1411 | 26 | 33 | 5670.0 | 31.765323 | 3794 | 10.666 | Critical | Increase Monitoring & Schedule Review | Lock A... |
| 2 | ISSUE-0203 | KEY-0203 | Unresolved Vulnerabilities from Latest Penetra... | 1 | Penetration Testing Effectiveness | Critical | In Progress | Reporter 8 | Assignee 18 | 2025-06-25 | ... | JP | 1260 | 26 | 17 | 6029.0 | 71.590986 | 7691 | 26.200 | Critical | Immediate System-wide Shutdown & Investigation... |
| 3 | ISSUE-0204 | KEY-0204 | Insufficient Access Control Measures | 1 | Control Effectiveness | Critical | In Progress | Reporter 10 | Assignee 5 | 2023-02-20 | ... | EU | 1084 | 28 | 13 | 3038.0 | 61.193139 | 4721 | 13.506 | Critical | Immediate System-wide Shutdown & Investigation... |
| 4 | ISSUE-0205 | KEY-0205 | Successful Phishing Attempt Targeting Executives | 1 | Phishing Attack | Medium | Open | Reporter 6 | Assignee 3 | 2024-06-12 | ... | FR | 976 | 26 | 6 | 587.5 | 67.685677 | 6103 | 17.574 | Critical | Isolate Affected System & Restrict User Access... |
5 rows × 32 columns
Normal & anomalous combined Data structure <class 'pandas.core.frame.DataFrame'> RangeIndex: 1600 entries, 0 to 1599 Data columns (total 33 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Issue ID 1600 non-null object 1 Issue Key 1600 non-null object 2 Issue Name 1600 non-null object 3 Issue Volume 1600 non-null int64 4 Category 1600 non-null object 5 Severity 1600 non-null object 6 Status 1600 non-null object 7 Reporters 1600 non-null object 8 Assignees 1600 non-null object 9 Date Reported 1600 non-null datetime64[ns] 10 Date Resolved 1600 non-null datetime64[ns] 11 Issue Response Time Days 1600 non-null int64 12 Impact Score 1600 non-null float64 13 Risk Level 1600 non-null object 14 Department Affected 1600 non-null object 15 Remediation Steps 1600 non-null object 16 Cost 1600 non-null float64 17 KPI/KRI 1600 non-null object 18 User ID 1600 non-null object 19 Timestamps 1600 non-null datetime64[ns] 20 Activity Type 1600 non-null object 21 User Location 1600 non-null object 22 IP Location 1600 non-null object 23 Session Duration in Second 1600 non-null int64 24 Num Files Accessed 1600 non-null int64 25 Login Attempts 1600 non-null int64 26 Data Transfer MB 1600 non-null float64 27 CPU Usage % 1600 non-null float64 28 Memory Usage MB 1600 non-null int64 29 Threat Score 1600 non-null float64 30 Threat Level 1600 non-null object 31 Defense Action 1600 non-null object 32 Color 1600 non-null object dtypes: datetime64[ns](3), float64(5), int64(6), object(19) memory usage: 412.6+ KB
None
Data statics summary
| count | mean | min | 25% | 50% | 75% | max | std | |
|---|---|---|---|---|---|---|---|---|
| Issue Volume | 1600.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 |
| Date Reported | 1600 | 2024-05-16 18:39:35.999999744 | 2023-01-01 00:00:00 | 2023-09-11 18:00:00 | 2024-05-22 12:00:00 | 2025-01-14 00:00:00 | 2025-09-18 00:00:00 | NaN |
| Date Resolved | 1600 | 2025-01-12 04:26:27.243045632 | 2023-01-03 00:00:00 | 2024-05-09 18:00:00 | 2025-08-25 00:00:00 | 2025-09-19 22:15:24.985807872 | 2025-09-24 00:00:00 | NaN |
| Issue Response Time Days | 1600.0 | 239.953125 | 1.0 | 6.0 | 10.0 | 471.0 | 992.0 | 314.477622 |
| Impact Score | 1600.0 | 50.521138 | 2.0 | 31.545 | 50.275 | 68.58 | 139.92 | 26.828678 |
| Cost | 1600.0 | 1474944.058437 | 126027.5 | 794263.25 | 1495187.75 | 2110045.0 | 2982839.0 | 775397.505099 |
| Timestamps | 1600 | 2024-05-17 06:24:11.400000256 | 2023-01-01 02:34:00 | 2023-09-12 03:29:00 | 2024-05-22 22:35:00 | 2025-01-14 06:18:30 | 2025-09-18 05:48:00 | NaN |
| Session Duration in Second | 1600.0 | 1276.593125 | 900.0 | 900.0 | 1000.0 | 1542.5 | 3314.0 | 506.765778 |
| Num Files Accessed | 1600.0 | 26.929375 | 26.0 | 26.0 | 26.0 | 26.0 | 46.0 | 2.567539 |
| Login Attempts | 1600.0 | 12.654375 | 3.0 | 9.0 | 12.0 | 17.0 | 35.0 | 5.766342 |
| Data Transfer MB | 1600.0 | 3273.810625 | 500.0 | 1315.75 | 2417.0 | 4290.625 | 18443.0 | 2757.678572 |
| CPU Usage % | 1600.0 | 49.599596 | 20.012005 | 35.126543 | 48.5276 | 64.489242 | 79.975415 | 17.245649 |
| Memory Usage MB | 1600.0 | 5475.488125 | 3003.0 | 4269.75 | 5438.5 | 6714.75 | 7995.0 | 1423.3196 |
| Threat Score | 1600.0 | 14.488103 | 2.5 | 10.7615 | 14.351 | 18.0945 | 33.684 | 5.589131 |
Normal & anomalous combined Data
| Issue ID | Issue Key | Issue Name | Issue Volume | Category | Severity | Status | Reporters | Assignees | Date Reported | ... | Session Duration in Second | Num Files Accessed | Login Attempts | Data Transfer MB | CPU Usage % | Memory Usage MB | Threat Score | Threat Level | Defense Action | Color | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ISSUE-0001 | KEY-0001 | Unauthorized Access Leading to Data Exposure | 1 | Data Breach | Low | Closed | Reporter 7 | Assignee 16 | 2023-12-07 | ... | 1002 | 26 | 6 | 3420.0 | 34.417556 | 7717 | 9.682 | Critical | Increase Monitoring & Schedule Review | Lock A... | Orange |
| 1 | ISSUE-0002 | KEY-0002 | Increased Exposure due to Insufficient Data En... | 1 | Risk Exposure | Low | In Progress | Reporter 1 | Assignee 4 | 2023-05-05 | ... | 1649 | 26 | 9 | 2825.0 | 38.368115 | 7828 | 14.314 | Critical | Increase Monitoring & Schedule Review | Lock A... | Orange |
| 2 | ISSUE-0003 | KEY-0003 | Non-Compliance with Data Protection Regulations | 1 | Legal Compliance | Medium | Closed | Reporter 3 | Assignee 6 | 2024-05-03 | ... | 2190 | 26 | 6 | 1022.5 | 21.429354 | 4263 | 18.496 | Critical | Isolate Affected System & Restrict User Access... | Orange-Red |
| 3 | ISSUE-0004 | KEY-0004 | Insufficient Coverage in Annual Risk Assessment | 1 | Risk Assessment Coverage | Low | Resolved | Reporter 3 | Assignee 17 | 2025-06-22 | ... | 907 | 36 | 18 | 2692.5 | 33.896298 | 6366 | 15.352 | Critical | Increase Monitoring & Schedule Review | Lock A... | Orange |
| 4 | ISSUE-0005 | KEY-0005 | Inconsistent Review of Security Policies | 1 | Management Oversight | High | In Progress | Reporter 7 | Assignee 13 | 2024-03-28 | ... | 900 | 42 | 3 | 3122.0 | 53.059948 | 5927 | 18.902 | Critical | Escalate to Security Operations Center (SOC) &... | Red |
5 rows × 33 columns
Key threat indicators Data structure
| KIT | Condition | Score | |
|---|---|---|---|
| 0 | Severity | Critical = 10, High = 8, Medium = 5, Low = 2 | 2 - 10 |
| 1 | Impact Score | 1 to 10 (already a score) | 1 - 10 |
| 2 | Risk Level | High = 8, Medium = 5, Low = 2 | 2 - 8 |
| 3 | Response Time | >7 days = 5, 3-7 days = 3, <3 days = 1 | 1 - 5 |
| 4 | Category | Unauthorized Access = 8, Phishing = 6, etc. | 1 - 8 |
| 5 | Activity Type | High-risk types (e.g., login, data_transfer) | 1 - 5 |
| 6 | Login Attempts | >5 = 5, 3-5 = 3, <3 = 1 | 1 - 5 |
| 7 | Num Files Accessed | >10 = 5, 5-10 = 3, <5 = 1 | 1 - 5 |
| 8 | Data Transfer MB | >100 MB = 5, 50-100 MB = 3, <50 MB = 1 | 1 - 5 |
| 9 | CPU Usage % | >80% = 5, 60-80% = 3, <60% = 1 | 1 - 5 |
| 10 | Memory Usage MB | >8000 MB = 5, 4000-8000 MB = 3, <4000 MB = 1 | 1 - 5 |
Scenarios with colors Data structure
| Scenario | Threat Level | Severity | Suggested Color | Rationale | |
|---|---|---|---|---|---|
| 0 | 1 | Critical | Critical | Dark Red | Maximum urgency, both threat and impact are cr... |
| 1 | 2 | Critical | High | Red | Very high risk, threat is critical and impact ... |
| 2 | 3 | Critical | Medium | Orange-Red | Significant threat but moderate impact. Act pr... |
| 3 | 4 | Critical | Low | Orange | High potential risk, current impact is minimal... |
| 4 | 5 | High | Critical | Red | High threat combined with critical impact. Nee... |
| 5 | 6 | High | High | Orange-Red | High threat and significant impact. Prioritize... |
| 6 | 7 | High | Medium | Orange | Elevated threat and moderate impact. Requires ... |
| 7 | 8 | High | Low | Yellow-Orange | High threat with low impact. Proactive monitor... |
| 8 | 9 | Medium | Critical | Orange | Moderate threat with critical impact. Prioriti... |
| 9 | 10 | Medium | High | Yellow-Orange | Medium threat with high impact. Needs resoluti... |
| 10 | 11 | Medium | Medium | Yellow | Medium threat and impact. Plan to address it. |
| 11 | 12 | Medium | Low | Light Yellow | Moderate threat, minimal impact. Monitor as ne... |
| 12 | 13 | Low | Critical | Yellow | Low threat but high impact. Address severity f... |
| 13 | 14 | Low | High | Light Yellow | Low threat with significant impact. Plan mitig... |
| 14 | 15 | Low | Medium | Green-Yellow | Low threat, moderate impact. Routine monitoring. |
| 15 | 16 | Low | Low | Green | Minimal risk. No immediate action required. |
6. Exploratory Data Analysis (EDA)¶
Foundational Phase for Cyber Threat Insight Modeling
Exploratory Data Analysis (EDA) is a critical first step in building effective cyber threat detection models. In this project, EDA was used to understand the structure, distribution, and relationships within the dataset before any modeling took place. The EDA process enabled the identification of key behavior patterns, data anomalies, and feature interactions essential for training accurate and interpretable machine learning models in a cybersecurity context.
6.1 Objective of EDA in Cybersecurity Modeling¶
- Identify data quality issues, distribution skews, and outliers that could bias or destabilize machine learning algorithms.
- Reveal temporal and behavioral patterns indicative of security incidents or suspicious activity.
- Uncover feature correlations and redundancies to support effective feature engineering.
- Provide statistical summaries and visual diagnostics to guide downstream modeling and threat hypothesis validation.
6.2 EDA Pipeline Components¶
1. Data Normalization¶
Function: normalize_numerical_features(p_df)
- Scales numerical features to a uniform 0–1 range using Min-Max Scaling.
- Ensures consistent feature magnitudes, which is vital for algorithms sensitive to scale (e.g., clustering, SVM).
Outcome: A normalized dataset prepared for consistent comparison and algorithmic input.
2. Temporal Trend Visualization¶
Function: plot_numerical_features_daily_values(...)
- Plots daily activity trends such as session duration, access counts, or data volumes.
- Supports detection of unusual spikes, seasonality, or activity bursts.
Outcome: Time-series charts that help detect behavioral anomalies tied to potential threats.
3. Statistical Feature Profiling¶
Functions: plot_histograms(df), plot_boxplots(df)
- Histograms reveal the shape of feature distributions and include overlays for mean, skewness, and kurtosis.
- Boxplots detect variability and extreme values such as large data transfers or excessive login attempts.
Outcome: In-depth distributional understanding and detection of outliers relevant for fraud and anomaly models.
4. Feature Interaction & Correlation Mapping¶
Functions: plot_scatter(...), plot_correlation_heatmap(...)
- Scatter plots examine relationships between behavioral indicators (e.g., login attempts vs. data exfiltration).
- Correlation heatmaps identify multicollinearity risks and guide feature selection.
Outcome: Improved understanding of behavioral interactions and reduced redundancy in model inputs.
5. Distribution Pipeline for Activity Features¶
Function: daily_distribution_of_activity_features_pipeline(df)
- Applies normalization and trend visualization across activity-related features.
- Supports daily, weekly, or monthly aggregation as required by operational cadence.
Outcome: Comparative trend analysis for baseline behavior modeling.
6. Integrated Visualization Dashboard¶
Function: combines_user_activities_scatter_plots_and_heatmap(...)
- Merges scatter plots and heatmaps into a single interface for analyst review.
- Facilitates multi-dimensional behavioral diagnostics.
Outcome: A cohesive visual layout for exploratory review and hypothesis generation.
7. Automated EDA Workflow¶
Function: exploratory_data_analysis_pipeline(...)
- Automates the full EDA process from normalization through visualization and diagnostics.
- Enables reproducibility and scalability across different datasets or time periods.
Outcome: Efficient and standardized EDA supporting rapid iteration and consistent insight delivery.
6.3 EDA Impact on Cyber Threat Modeling¶
| EDA Outcome | Modeling Benefit |
|---|---|
| Normalized Features | Enables fair weighting and faster convergence in model training |
| Outlier Detection | Prevents skewed predictions and informs anomaly modeling |
| Feature Relationships | Supports intelligent feature selection and dimensionality reduction |
| Time-Based Trend Analysis | Helps identify suspicious behavior patterns (e.g., data spikes) |
| Correlation Heatmaps | Flags redundant inputs that may distort model logic |
6.4 Summary of Benefits¶
- Model Readiness: Ensures clean, well-scaled, and insightful features.
- Threat Hypothesis Validation: Validates known behavioral patterns through visual and statistical evidence.
- Anomaly Detection Prep: Identifies irregularities early, enhancing unsupervised modeling approaches.
- Scalability & Reusability: Modular design supports reuse in future cyber datasets and use cases.
In [ ]:
def normalize_numerical_features(p_df):
scaler = MinMaxScaler()
p_df_daily = p_df.copy()
df_normalized = pd.DataFrame(scaler.fit_transform(p_df_daily), columns=p_df_daily.columns.to_list(), index=p_df_daily.index)
return df_normalized
#------------------------------------------------------------------
def plot_numerical_features_daily_values(df, date_column, feature_columns, rows, cols):
fig, axes = plt.subplots(rows, cols, figsize=(16, 8))
axes = axes.flatten() # Flatten the 2D array of axes for easier iteration
for i, column in enumerate(feature_columns):
ax = axes[i]
ax.plot(df.index, df[column], marker='o', label=column, color='b')
ax.set_title(column, fontsize=10)
ax.set_xlabel("Date Reported", fontsize=8)
ax.set_ylabel(column, fontsize=8)
ax.grid(True)
ax.legend(fontsize=8)
# Format x-axis to prevent overlapping
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax.xaxis.set_major_locator(mdates.DayLocator(interval=100)) # Show every 100 days
plt.setp(ax.xaxis.get_majorticklabels(), rotation=45, ha="right", fontsize=8)
# Hide any unused subplots
for j in range(len(feature_columns), len(axes)):
axes[j].set_visible(False)
plt.tight_layout()
plt.show()
#------------------------------------------------------------------
def daily_distribution_of_activity_features_pipeline(df):
"""
Pipeline to plot daily distribution of numerical features.
"""
features = df.columns.tolist()
n_features = len(features)
rows = int(n_features/4)
cols = int(n_features/2)
print("Non normalized daily distribution")
plot_numerical_features_daily_values(df, "Date Reported", features, rows, cols)
#plot_numerical_features_daily_values(df)
print("Normalized daily distribution")
df_normalized = normalize_numerical_features(df)
#plot_numerical_features_daily_values(df_normalized)
plot_numerical_features_daily_values(df_normalized, "Date Reported", features, rows, cols)
#-------------------------------------------------------------------------
def plot_histograms(df):
"""
Plots histograms for all features in the list with risk level and displays basic statistics.
"""
# Define the risk palette
risk_palette = {
'Low': 'green',
'Medium': 'yellow',
'High': 'orange',
'Critical': 'red'
}
features = df.columns.tolist()
n_features = len(features)
n_cols = int(n_features/2)
n_rows = int((n_features + n_cols - 1) // n_cols) # Calculate rows needed for the grid
fig, axes = plt.subplots(n_rows, n_cols, figsize=(5 * n_cols, n_rows * 6)) # Dynamically adjust figure size
axes = np.array(axes) # Ensure `axes` is always an array
axes = axes.flatten() # Flatten to handle indexing consistently
for i, feature in enumerate(features):
#sns.histplot(df[feature], bins=30, kde=True, ax=axes[i])
if df[feature].dtype == 'object' and set(df[feature].unique()).issubset(risk_palette.keys()):
sns.histplot(df[feature], palette=risk_palette, ax=axes[i])
else:
sns.histplot(df[feature], bins=30, kde=True, ax=axes[i])
axes[i].set_title(f'Histogram of {feature}')
axes[i].set_xlabel(feature)
axes[i].set_ylabel('Frequency')
# Calculate and display statistics for numeric features
if np.issubdtype(df[feature].dtype, np.number):
mean_return = df[feature].mean()
std_dev = df[feature].std()
skewness = df[feature].skew()
kurtosis = df[feature].kurtosis()
# Calculate and display statistics for numeric features
if np.issubdtype(df[feature].dtype, np.number):
statistics = (f"Mean: {mean_return:.4f}\n"
f"Std Dev: {std_dev:.4f}\n"
f"Skewness: {skewness:.4f}\n"
f"Kurtosis: {kurtosis:.4f}")
axes[i].text(0.35, -0.18, statistics, transform=axes[i].transAxes,
fontsize=10, verticalalignment='top',
bbox=dict(boxstyle="round,pad=0.3", edgecolor="black", facecolor="lightgrey"))
# Hide any unused subplots
for j in range(n_features, len(axes)):
axes[j].set_visible(False)
#plt.tight_layout()
plt.tight_layout(rect=[0, 0.05, 1, 1]) # Add padding to the bottom
plt.show()
def plot_boxplots(df):
"""
Plots boxplots for all features in the list and displays basic statistics.
"""
# Define the risk palette
risk_palette = {
'Low': 'green',
'Medium': 'yellow',
'High': 'orange',
'Critical': 'red'
}
features = df.columns.tolist()
n_features = len(features)
n_cols = int(n_features/2)
n_rows = int((n_features + n_cols - 1) // n_cols) # Calculate rows needed for the grid
fig, axes = plt.subplots(n_rows, n_cols, figsize=(5 * n_cols, n_rows * 6)) # Dynamically adjust figure size
axes = np.array(axes) # Ensure `axes` is always an array
axes = axes.flatten() # Flatten to handle indexing consistently
for i, feature in enumerate(features):
#sns.boxplot(y=df[feature], ax=axes[i])
# Check if the feature has risk levels
if df[feature].dtype == 'object' and set(df[feature].unique()).issubset(risk_palette.keys()):
sns.boxplot(y=df[feature], palette=risk_palette, ax=axes[i])
else:
sns.boxplot(y=df[feature], ax=axes[i])
axes[i].set_title(f'Boxplot of {feature}')
axes[i].set_ylabel(feature)
# Calculate and display statistics for numeric features
if np.issubdtype(df[feature].dtype, np.number):
mean_return = df[feature].mean()
std_dev = df[feature].std()
skewness = df[feature].skew()
kurtosis = df[feature].kurtosis()
# Add statistics below the plot
statistics = (f"Mean: {mean_return:.4f}\n"
f"Std Dev: {std_dev:.4f}\n"
f"Skewness: {skewness:.4f}\n"
f"Kurtosis: {kurtosis:.4f}")
axes[i].text(0.35, -0.18, statistics, transform=axes[i].transAxes,
fontsize=10, verticalalignment='top',
bbox=dict(boxstyle="round,pad=0.3", edgecolor="black", facecolor="lightgrey"))
# Hide any unused subplots
for j in range(n_features, len(axes)):
axes[j].set_visible(False)
#plt.tight_layout()
plt.tight_layout(rect=[0, 0.05, 1, 1]) # Add padding to the bottom
plt.show()
#-----------------------------------------------------------------------------------------------------
def visualize_form_of_activity_features_distribution(df):
"""
Master function to plot histograms and boxplots for all features, with statistics.
"""
sns.set(style="whitegrid")
print("Plotting histograms...")
plot_histograms(df)
print("Plotting boxplots...")
plot_boxplots(df)
def plot_scatter(axes, x, y, hue, df, palette, title, xlabel, ylabel, legend_title, ax_index):
"""
Creates a scatter plot on the specified axis.
"""
sns.scatterplot(x=x, y=y, hue=hue, data=df, palette=palette, ax=axes[ax_index])
axes[ax_index].set_title(title)
axes[ax_index].set_xlabel(xlabel)
axes[ax_index].set_ylabel(ylabel)
axes[ax_index].legend(title=legend_title)
def plot_correlation_heatmap(axes, df, features, ax_index):
"""
Creates a heatmap showing the correlation between selected features.
"""
# Select only numerical features
numeric_features = df[features].select_dtypes(include=['number'])
# Calculate the correlation matrix
corr_matrix = numeric_features.corr()
# Plot the heatmap
sns.heatmap(corr_matrix, annot=True, fmt=".2f", cmap="coolwarm", cbar=True, ax=axes[ax_index])
axes[ax_index].set_title("Correlation Heatmap of Numerical Features")
#sns.heatmap(df[features].corr(), annot=True, cmap="coolwarm", fmt=".2f", ax=axes[ax_index])
#axes[ax_index].set_title("Correlation Heatmap")
def combines_user_activities_scatter_plots_and_heatmap(scatter_df, df):
"""
Combines scatter plots and heatmap into a single figure using subplots.
"""
fig, axes = plt.subplots(1, 3, figsize=(24, 8)) # Create subplots (1 row, 3 columns)
# Plot 1: Session Duration vs Data Transfer
plot_scatter(
axes=axes,
x="Session Duration in Second",
y="Data Transfer MB",
hue="User Location",
df=scatter_df,
palette="Set1",
title="Session Duration vs Data Transfer (MB) by Location",
xlabel="Session Duration (seconds)",
ylabel="Data Transfer (MB)",
legend_title="User Location",
ax_index=0
)
# Plot 2: Login Attempts vs Data Transfer
plot_scatter(
axes=axes,
x="Login Attempts",
y="Data Transfer MB",
hue="User Location",
df=scatter_df,
palette="Set2",
title="Login Attempts vs Data Transfer (MB) by Location",
xlabel="Login Attempts",
ylabel="Data Transfer (MB)",
legend_title="User Location",
ax_index=1
)
# Plot 3: Correlation Heatmap
plot_correlation_heatmap(
axes=axes,
df=df,
features=df.columns,
ax_index=2
)
# Adjust layout and show plot
plt.tight_layout()
plt.show()
#-----------------------------------------Main EDA pipeline------------------------------------------------------
def explaratory_data_analysis_pipeline():
file_path_to_normal_and_anomalous_google_drive = \
"/content/drive/My Drive/Cybersecurity Data/normal_and_anomalous_cybersecurity_dataset_for_google_drive_kb.csv"
eda_features = [
"Date Reported", "Issue Response Time Days", "Impact Score", "Cost",
"Session Duration in Second", "Num Files Accessed", "Login Attempts",
"Data Transfer MB", "CPU Usage %", "Memory Usage MB", "Threat Score"
]
activity_features = [
"Risk Level", "Threat Level", "Issue Response Time Days", "Impact Score", "Cost",
"Session Duration in Second", "Num Files Accessed", "Login Attempts",
"Data Transfer MB", "CPU Usage %", "Memory Usage MB", "Threat Score"
]
#load real_world_simulated_normal_and_anomalous_df
df = pd.read_csv(file_path_to_normal_and_anomalous_google_drive)
reporting_frequency = 'Quarter'
frequency = reporting_frequency[0].upper()
if reporting_frequency.capitalize() == 'Month' or reporting_frequency.capitalize() == 'Quarter':
frequency_date_column = reporting_frequency.capitalize() + '_Year'
frequency_date_column = reporting_frequency.capitalize() + '_Year'
eda_features_df = df[eda_features].copy()
eda_features_df = eda_features_df.set_index("Date Reported")
freq_eda_features_df = eda_features_df.copy()
freq_eda_features_df[frequency_date_column] = pd.to_datetime(freq_eda_features_df.index)
freq_eda_features_df[frequency_date_column] = freq_eda_features_df[frequency_date_column].dt.to_period(frequency)
freq_eda_features_df = freq_eda_features_df.groupby(frequency_date_column).mean()
#df['Date Reported'] = df['Date Reported'].dt.to_timestamp()
freq_eda_features_df.index = freq_eda_features_df.index.to_timestamp()
display(freq_eda_features_df)
activity_features_df = df[activity_features].copy()
scatter_plot_features_df = df[["Session Duration in Second", "Login Attempts",
"Data Transfer MB", "User Location"]].copy()
#daily_distribution_of_activity_features_pipeline(eda_features_df )
daily_distribution_of_activity_features_pipeline(freq_eda_features_df )
visualize_form_of_activity_features_distribution(activity_features_df)
combines_user_activities_scatter_plots_and_heatmap(scatter_plot_features_df, activity_features_df)
return freq_eda_features_df
if __name__ == "__main__":
real_world_normal_and_anomalous_df = explaratory_data_analysis_pipeline()
| Issue Response Time Days | Impact Score | Cost | Session Duration in Second | Num Files Accessed | Login Attempts | Data Transfer MB | CPU Usage % | Memory Usage MB | Threat Score | |
|---|---|---|---|---|---|---|---|---|---|---|
| Quarter_Year | ||||||||||
| 2023-01-01 | 521.214815 | 49.838296 | 1.463431e+06 | 1202.592593 | 27.200000 | 13.214815 | 3275.107407 | 50.057681 | 5284.200000 | 14.315067 |
| 2023-04-01 | 409.666667 | 47.391533 | 1.457252e+06 | 1277.026667 | 26.726667 | 12.360000 | 3400.583333 | 50.050250 | 5310.946667 | 13.869973 |
| 2023-07-01 | 310.119718 | 51.402535 | 1.378840e+06 | 1218.514085 | 27.042254 | 12.190141 | 3437.031690 | 50.768242 | 5353.908451 | 14.640014 |
| 2023-10-01 | 356.134752 | 48.684113 | 1.561511e+06 | 1278.049645 | 26.666667 | 12.659574 | 3736.014184 | 50.721301 | 5507.468085 | 14.076184 |
| 2024-01-01 | 299.451613 | 52.413161 | 1.490227e+06 | 1377.161290 | 27.477419 | 13.012903 | 3180.580645 | 47.191147 | 5607.019355 | 14.916826 |
| 2024-04-01 | 210.021127 | 51.438099 | 1.587999e+06 | 1280.154930 | 26.704225 | 12.042254 | 3138.404930 | 49.657295 | 5529.014085 | 14.603817 |
| 2024-07-01 | 192.870748 | 48.618912 | 1.488855e+06 | 1420.170068 | 26.952381 | 12.591837 | 3050.500000 | 48.680772 | 5698.802721 | 14.166639 |
| 2024-10-01 | 148.440252 | 52.251887 | 1.486664e+06 | 1272.716981 | 27.119497 | 12.861635 | 3285.113208 | 48.954714 | 5460.150943 | 14.895660 |
| 2025-01-01 | 109.724638 | 51.035870 | 1.432180e+06 | 1242.659420 | 26.601449 | 12.688406 | 3311.887681 | 50.919250 | 5683.239130 | 14.547029 |
| 2025-04-01 | 68.066667 | 52.257333 | 1.360314e+06 | 1216.690909 | 27.036364 | 12.454545 | 2897.284848 | 50.537419 | 5479.909091 | 14.862073 |
| 2025-07-01 | 26.142857 | 49.877857 | 1.539493e+06 | 1244.452381 | 26.579365 | 13.206349 | 3385.246032 | 48.110067 | 5280.920635 | 14.347794 |
Non normalized daily distribution
Normalized daily distribution
Plotting histograms...
Plotting boxplots...
Feature Engineering¶
The feature engineering process in our Cyber Threat Insight project was strategically designed to simulate realistic cyber activity, enhance anomaly visibility, and prepare a high-quality dataset for training robust threat classification models. Given the natural rarity and imbalance of cybersecurity anomalies, we adopted a multi-step workflow combining statistical simulation, normalization, feature selection, explainability, and data augmentation.
Feature Engineering Flowchart¶
In [ ]:
from graphviz import Digraph
from IPython.display import Image
# Create a directed graph
#dot = Digraph(comment='Cyber Threat Insight - Feature Engineering Workflow', format='png')
dot = Digraph("Cyber Threat Insight - Feature Engineering Workflow", format="png")
# Feature Engineering Phases
dot.node('Start', 'Start')
dot.node('DataInj', 'Data Injection\n(Cholesky-Based Perturbation)', shape='box', style='filled', fillcolor='lightblue')
dot.node('Scaling', 'Feature Normalization & Scaling\n(Min-Max, Z-score)', shape='box', style='filled', fillcolor='lightgray')
dot.node('CorrHeat', 'Correlation Heatmap Analysis\n(Pearson/Spearman)', shape='box', style='filled', fillcolor='orange')
dot.node('FeatImp', 'Feature Importance\n(Random Forest)', shape='box', style='filled', fillcolor='gold')
dot.node('SHAP', 'Model Explainability\n(SHAP Values)', shape='box', style='filled', fillcolor='lightgreen')
dot.node('PCA', 'PCA & Variance Explained\n(Scree Plot)', shape='box', style='filled', fillcolor='plum')
dot.node('Augment', 'Data Augmentation\n(SMOTE, GAN)', shape='box', style='filled', fillcolor='lightpink')
dot.node('End', 'Feature Set Ready for Modeling', shape='ellipse', style='filled', fillcolor='lightyellow')
# Arrows to show workflow
dot.edge('Start', 'DataInj')
dot.edge('DataInj', 'Scaling')
dot.edge('Scaling', 'CorrHeat')
dot.edge('CorrHeat', 'FeatImp')
dot.edge('FeatImp', 'SHAP')
dot.edge('SHAP', 'PCA')
dot.edge('PCA', 'Augment')
dot.edge('Augment', 'End')
features_engineering_flowchart = dot.render("features_engineering_flowchart", format="png", cleanup=False)
display(Image(filename="features_engineering_flowchart.png"))
print("Flowchart generated successfully!")
Flowchart generated successfully!
1. Synthetic Data Loading¶
We began with a synthetic dataset that simulates real-time user sessions and system behaviors, including attributes such as login attempts, session duration, data transfer, and system resource usage. This dataset serves as a safe and flexible baseline to emulate both normal and suspicious behaviors without exposing sensitive infrastructure data.
2. Anomaly Injection – Cholesky-Based Perturbation¶
To introduce statistically sound anomalies, we applied a Cholesky decomposition-based perturbation to the feature covariance matrix. This method creates subtle but realistic multivariate deviations in the dataset, reflecting how actual threats often manifest through combinations of unusual behaviors (e.g., high data transfer coupled with long session durations).
3. Feature Normalization¶
All numerical features were normalized using a combination of Min-Max Scaling and Z-score Standardization. This step ensures that features with different units or scales (e.g., memory usage vs. login attempts) contribute equally during model training, especially for distance-based algorithms.
4. Correlation Analysis¶
Using Pearson and Spearman correlation heatmaps, we examined inter-feature relationships to detect multicollinearity. This analysis helped eliminate redundant features and highlighted meaningful operational linkages between system metrics, such as correlations between CPU and memory usage during suspicious sessions.
5. Feature Importance (Random Forest)¶
We trained a Random Forest classifier to compute feature importance scores. These scores provided insights into which features had the most predictive power for classifying threat levels, enabling targeted refinement of the feature set.
6. Model Explainability (SHAP Values)¶
To ensure model transparency, we applied SHAP (SHapley Additive exPlanations) for both global and local interpretability. SHAP values quantify how each feature impacts the model’s decisions for individual predictions, which is critical for cybersecurity analysts needing to validate threat classifications.
7. Dimensionality Reduction (PCA)¶
We employed Principal Component Analysis (PCA) to reduce feature dimensionality while retaining maximum variance. A scree plot was used to identify the optimal number of components. This step improves computational efficiency and enhances model generalization.
8. Data Augmentation (SMOTE and GANs)¶
To address the significant class imbalance between benign and malicious sessions, we applied two augmentation strategies:
- SMOTE (Synthetic Minority Over-sampling Technique) to interpolate new synthetic samples for underrepresented classes.
- Generative Adversarial Networks (GANs) to produce high-fidelity, realistic threat scenarios that further enrich the minority class.
Outcome¶
Through this comprehensive workflow, we generated a clean, balanced, and interpretable feature set optimized for training machine learning models. This feature engineering pipeline enabled the system to detect nuanced threat patterns while maintaining explainability and performance across diverse threat levels.
In [ ]:
#from json import load
# -----Save df_fe, label_encoders anf numerical columns scaler to to your Google Drive---------------------
def save_objects_to_drive(df_fe,
cat_cols_label_encoders,
num_fe_scaler,
filepath_df="/content/drive/My Drive/Cybersecurity Data/df_fe.pkl",
filepath_cat_cols_label_encoders="/content/drive/My Drive/Model deployment/cat_cols_label_encoders.pkl",
filepath_num_fe_scaler="/content/drive/My Drive/Model deployment/ num_fe_scaler.pkl"):
try:
# Ensure the directory exists for df_fe
df_directory = os.path.dirname(filepath_df)
if not os.path.exists(df_directory):
os.makedirs(df_directory)
print(f"Created directory: {df_directory}")
# Ensure the directory exists for label_encoders and scaler
model_directory = os.path.dirname(filepath_cat_cols_label_encoders)
if not os.path.exists(model_directory):
os.makedirs(model_directory)
print(f"Created directory: {model_directory}")
with open(filepath_df, 'wb') as f:
pickle.dump(df_fe, f)
print(f"DataFrame saved successfully to: {filepath_df}")
with open(filepath_cat_cols_label_encoders, 'wb') as f:
pickle.dump(cat_cols_label_encoders, f)
print(f"Label encoders saved successfully to: {filepath_cat_cols_label_encoders}")
with open(filepath_num_fe_scaler, 'wb') as f:
pickle.dump(num_fe_scaler, f)
print(f"Label encoders saved successfully to: {filepath_num_fe_scaler}")
except Exception as e:
print(f"An error occurred while saving: {e}")
# ----------------------------------Load df_fe and label_encoders from your Google Drive-----------------------------------------
def load_objects_from_drive(filepath_df="/content/drive/My Drive/Cybersecurity Data/df_fe.pkl",
filepath_cat_cols_label_encoders="/content/drive/My Drive/Model deployment/cat_cols_label_encoders.pkl",
filepath_num_fe_scaler="/content/drive/My Drive/Model deployment/ num_fe_scaler.pkl"):
try:
with open(filepath_df, 'rb') as f:
df_fe = pickle.load(f)
print(f"DataFrame loaded successfully from: {filepath_df}")
with open(filepath_cat_cols_label_encoders, 'rb') as f:
cat_cols_label_encoders = pickle.load(f)
print(f"Label encoders loaded successfully from: {filepath_cat_cols_label_encoders}")
with open(filepath_num_fe_scaler, 'rb') as f:
num_fe_scaler = pickle.load(f)
print(f"Label encoders loaded successfully from: {filepath_num_fe_scaler}")
return df_fe, cat_cols_label_encoders, num_fe_scaler
except Exception as e:
print(f"An error occurred while loading: {e}")
return None, None, None # Return None for the third value as well
#-------------Generate Synthetic Anomalies Using Cholesky-Based Perturbation-------------------
def get_files_path(
normal_operations_file_path = "/content/drive/My Drive/Cybersecurity Data/normal_and_anomalous_cybersecurity_dataset_for_google_drive_kb.csv",
combined_normal_and_anomaly_file_path = "/content/combined_normal_and_anomaly_output_file_for_google_drive_kb.csv"):
return {
"normal_operations_file_path": normal_operations_file_path,
"combined_normal_and_anomaly_file_path": combined_normal_and_anomaly_file_path
}
def load_Synthetic_dataset(filepath):
return pd.read_csv(filepath)
def scale_data(df, features):
scaler = StandardScaler()
features_to_scale = [f for f in features if f != 'Timestamps']
scaled = scaler.fit_transform(df[features_to_scale].dropna())
return scaled, scaler
def cholesky_decomposition(scaled_data):
cov_matrix = np.cov(scaled_data, rowvar=False)
L = np.linalg.cholesky(cov_matrix)
return L
def generate_cholesky_anomalies(real_data, L, num_samples=1000):
np.random.seed(42)
normal_samples = np.random.randn(num_samples, real_data.shape[1])
synthetic_anomalies = normal_samples @ L.T
return synthetic_anomalies
def inverse_transform(synthetic_data, scaler):
return scaler.inverse_transform(synthetic_data)
def create_anomaly_df(original_data, synthetic_original, features):
df_synthetic = pd.DataFrame(synthetic_original, columns=features)
# Create full column DataFrame for synthetic data with same structure as original
df_synthetic_full = pd.DataFrame(columns=original_data.columns)
# Fill known numerical features
for col in features:
df_synthetic_full[col] = df_synthetic[col]
# Fill in the rest of the columns using random sampling or generation
for col in original_data.columns:
if col not in features:
if original_data[col].dtype == 'object':
df_synthetic_full[col] = np.random.choice(original_data[col].dropna().unique(), size=len(df_synthetic_full))
elif np.issubdtype(original_data[col].dtype, np.datetime64):
# If timestamps exist, shift a base date with random offsets
base = pd.to_datetime("2024-01-01")
df_synthetic_full[col] = base + pd.to_timedelta(np.random.randint(0, 90, size=len(df_synthetic_full)), unit='D')
else:
df_synthetic_full[col] = np.random.choice(original_data[col].dropna(), size=len(df_synthetic_full))
#df_synthetic_full["Threat Level"] = "Anomalous"
df_synthetic_full["Source"] = "Synthetic"
df_real = original_data.copy()
df_real["Source"] = "Real"
df_combined = pd.concat([df_real, df_synthetic_full], ignore_index=True)
return df_combined
def save_dataset(df, path):
df.to_csv(path, index=False)
print(f"Saved combined dataset with synthetic anomalies to: {path}")
def data_injection_cholesky_based_perturbation(file_paths = "", save_data_true_false = True):
print("Anomaly Injection – Cholesky-Based Perturbation...")
if save_data_true_false == True:
file_paths = get_files_path()
df_real = load_Synthetic_dataset(file_paths["normal_operations_file_path"])
else:
df_real = load_Synthetic_dataset(file_paths)
#df_real.info()
#display(df_real.head())
numerical_columns_for_scaling = [col for col in numerical_columns if col != "Timestamps"]
scaled_data, scaler = scale_data(df_real, numerical_columns_for_scaling)
L = cholesky_decomposition(scaled_data)
synthetic_scaled = generate_cholesky_anomalies(df_real[numerical_columns_for_scaling], L, num_samples=100)
synthetic_original = inverse_transform(synthetic_scaled, scaler)
normal_and_combined_cholesky_based_perturbation_df = create_anomaly_df(df_real, synthetic_original, numerical_columns_for_scaling)
#normal_and_combined_cholesky_based_perturbation_df.info()
#display(normal_and_combined_cholesky_based_perturbation_df.head())
if save_data_true_false == True:
save_dataset(normal_and_combined_cholesky_based_perturbation_df, file_paths["combined_normal_and_anomaly_file_path"])
return normal_and_combined_cholesky_based_perturbation_df
# -------------------------------Normalize numerical feature--------------------------------------
def normalize_numerical_features(df, p_numerical_columns):
# normalized_df, scaler = scale_data(df, p_numerical_columns)
# return normalized_df, scaler
scaler = MinMaxScaler()
df[p_numerical_columns] = scaler.fit_transform(df[p_numerical_columns])
return df, scaler # Return the DataFrame and scaler
def encode_dates(df, date_columns):
"""
Extracts date components from specified columns in a DataFrame.
Parameters:
df (DataFrame): The DataFrame containing date columns.
date_columns (list): List of date columns to extract components from.
Returns:
DataFrame: DataFrame with additional date component columns.
"""
processed_df = df.copy()
for date_col in date_columns:
# Convert the column to datetime if it's not already
processed_df[date_col] = pd.to_datetime(processed_df[date_col], errors='coerce')
# Check if the column is a datetime column before applying .dt accessor
if pd.api.types.is_datetime64_any_dtype(processed_df[date_col]):
processed_df[f"year_{date_col}"] = processed_df[date_col].dt.year
processed_df[f"month_{date_col}"] = processed_df[date_col].dt.month
processed_df[f"day_{date_col}"] = processed_df[date_col].dt.day
processed_df[f"day_of_week_{date_col}"] = processed_df[date_col].dt.dayofweek # Monday=0, Sunday=6
processed_df[f"day_of_year_{date_col}"] = processed_df[date_col].dt.dayofyear
else:
print(f"Warning: Column '{date_col}' is not a datetime column and will be skipped.")
# Example of converting timestamps to seconds (if a timestamp column exists)
if "Timestamps" in date_columns:
processed_df["timestamp_seconds"] = processed_df["Timestamps"].astype(int) / 10**9
return processed_df.drop(columns=date_columns)
def encode_categorical_columns(df, categorical_columns):
"""
Applies label encoding to specified categorical columns in a DataFrame.
Parameters:
df (DataFrame): The DataFrame containing categorical columns.
categorical_columns (list): List of columns to apply label encoding to.
Returns:
DataFrame, dict: DataFrame with encoded categorical columns and a dictionary of label encoders.
"""
processed_df = df.copy()
label_encoders = {}
for column in categorical_columns:
le = LabelEncoder()
processed_df[column] = le.fit_transform(processed_df[column].astype(str))
label_encoders[column] = le
return processed_df, label_encoders
def decode_categorical_columns( df_to_decode, label_encoders):
"""
Decodes label-encoded categorical columns in a DataFrame.
Parameters:
df_to_decode (DataFrame): The DataFrame containing label-encoded categorical columns.
label_encoders (dict): Dictionary of LabelEncoders used for encoding, with column names as keys.
Returns:
DataFrame: DataFrame with decoded categorical columns.
"""
# initialize decoded data frame(decoded_df)
decoded_df = [[]]
processed_df_to_decode = df_to_decode.copy()
for column, le in label_encoders.items():
if column in processed_df_to_decode.columns:
decoded_df[column] = le.inverse_transform(processed_df_to_decode[column])
return decoded_df
def preprocess_dataframe(df, numerical_columns, date_columns, categorical_columns):
"""
Main function to preprocess a DataFrame by encoding dates and categorical columns.
Parameters:
df (DataFrame): Original DataFrame to be copied and processed.
numerical_columns (list): List of numerical columns (currently unused in this function).
date_columns (list): List of date columns to extract components from.
categorical_columns (list): List of categorical columns to encode.
Returns:
DataFrame, dict: Processed DataFrame and dictionary of label encoders.
"""
#Normalize numerical feature
#processed_df = normalize_numerical_features(df, numerical_columns)
df, normalize_numerical_features_scaler = normalize_numerical_features(df, [i for i in numerical_columns if i not in ['Timestamps']])
# Apply date encoding using the df
processed_df = encode_dates(df, date_columns) # Use the output of normalize_numerical_features
# Apply categorical encoding using the processed_df, but exclude date_columns
processed_df, categorical_columns_label_encoders = encode_categorical_columns(processed_df, [col for col in categorical_columns if col not in date_columns])
return processed_df, categorical_columns_label_encoders, normalize_numerical_features_scaler # Return processed_df instead of df
#-------------------------------------------------------------------------------------------------------------------------
# 1. Correlation Heatmap
def plot_correlation_heatmap(ax, df, method='pearson'):
numeric_df = df.select_dtypes(include=[np.number])
corr = numeric_df.corr(method=method)
sns.heatmap(corr, cmap='coolwarm', annot=False, fmt='.2f', square=True, ax=ax)
ax.set_title(f'{method.capitalize()} Correlation Heatmap')
# 2. Feature Importance
def plot_feature_importance(ax, X, y, top_n=None):
rf = RandomForestClassifier(n_estimators=100, random_state=42)
rf.fit(X, y)
importances = rf.feature_importances_
if top_n is None or top_n > len(importances):
top_n = len(importances)
indices = np.argsort(importances)[-top_n:]
ax.barh(range(top_n), importances[indices], align='center')
ax.set_yticks(range(top_n))
ax.set_yticklabels([X.columns[i] for i in indices])
ax.set_xlabel("Feature Importance")
ax.set_title("Top Random Forest Feature Importances")
return rf
# 3. SHAP Summary Plot (Standalone, not in subplot)
# Function to set font properties for plot axes
def set_font_properties(ax, x_fontsize=8, y_fontsize=8, labelcolor='black', mean_shap_fontsize=8, font_name = 'sans-serif'):
"""
Set the font properties for axes ticks.
Args:
- ax: The axes object for the plot
- x_fontsize: Font size for x-axis labels
- y_fontsize: Font size for y-axis labels
- labelcolor: Color for the labels (default is 'blue')
"""
ax.tick_params(axis='x', labelsize=x_fontsize, labelcolor=labelcolor)
ax.tick_params(axis='y', labelsize=y_fontsize, labelcolor=labelcolor)
# Adjust the font for the x-axis labels
for label in ax.get_xticklabels():
label.set_fontsize(x_fontsize) # Set font size
label.set_fontname(font_name) # Default sans-serif font
label.set_color(labelcolor) # Set label color
# Adjust the font for the y-axis labels
for label in ax.get_yticklabels():
label.set_fontsize(y_fontsize) # Set font size
label.set_fontname(font_name) # Default sans-serif font
label.set_color(labelcolor) # Set label color
# Adjust mean(|SHAP value|) font size (located in the text below the plot)
for text in ax.texts:
if 'mean(|SHAP value|)' in text.get_text():
text.set_fontsize(mean_shap_fontsize) # Reduce the font size for the mean(|SHAP value|) text
text.set_fontname(font_name) # Default sans-serif font
text.set_color(labelcolor) # Set label color
# Function to update plot title font
def update_title(title, fontsize=8, family='sans-serif', fontweight='normal'):
"""
Update the title of the plot with custom font properties.
Args:
- title: Title of the plot
- fontsize: Font size for the title
- family: Font family for the title
- fontweight: Font weight for the title
"""
plt.title(title, fontsize=fontsize, family=family, fontweight=fontweight)
def smaller_shap_summary_plot(shap_values, X, y, plot_type="bar", plot_size=(70, 30), title="SHAP Summary Plot"):
"""
Generates a smaller SHAP summary plot.
Args:
shap_values: SHAP values (output from SHAP model explainer)
X: The feature matrix(Sample data used for generating the SHAP plot)
plot_type: The type of plot ("dot", "bar", etc.).
plot_size: A tuple (width, height) specifying the plot's size in inches.
title: Custom title for the plot (default is 'SHAP Summary Plot')
"""
labels = sorted(list(set(y) | set(y)))
level_mapping = {0: "Low", 1: "Medium", 2: "High", 3: "Critical"}
class_names = [level_mapping.get(label) for label in labels]
shap.summary_plot(shap_values, X, plot_type=plot_type, show=False) #prevent auto showing the plot, so we can modify it.
#shap.summary_plot(shap_values, X, plot_type=plot_type, feature_names=list(X), class_names=class_names, show=False)
plt.tight_layout() #reduce white space around plot.
# Access the current axes (for summary plot)
ax = plt.gca()
# Change font properties for feature names and axis labels
set_font_properties(ax)
# Update the title with custom font
update_title(title)
plt.show() #manually show it.
def plot_shap_summary(model, X_sample, y):
#level_mapping = {"Low": 0, "Medium": 1, "High": 2, "Critical": 3}
#class_names = list(level_mapping.keys())
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_sample)
if isinstance(shap_values, list) and len(shap_values) > 1:
#shap.summary_plot(shap_values[1], X_sample, plot_size=(2, 2)) # Binary case
smaller_shap_summary_plot(shap_values[1], X_sample, y)
else:
#shap.summary_plot(shap_values, X_sample, plot_size=(2, 2))
# Generate summary plot with custom class names in the legend
smaller_shap_summary_plot(shap_values, X_sample, y)
# 4. PCA Scree Plot
def plot_pca_variance(ax, X, threshold=0.95):
pca = PCA().fit(X)
cum_var = np.cumsum(pca.explained_variance_ratio_)
ax.plot(cum_var, marker='o', linestyle='--', color='b')
ax.axhline(y=threshold, color='r', linestyle='-')
ax.set_title("PCA Scree Plot")
ax.set_xlabel("Num Components")
ax.set_ylabel("Cumulative Explained Variance")
ax.grid(True)
# 5. Main Driver Function
def run_feature_analysis(df_fe, target_column="Threat Level", corr_method="pearson"):
print("Running Feature Analysis Pipeline...")
df_local = df_fe.copy()
# Encode target if needed
if df_local[target_column].dtype == 'object':
le = LabelEncoder()
df_local[target_column] = le.fit_transform(df_local[target_column])
X = df_local.select_dtypes(include=[np.number]).drop(columns=[target_column], errors='ignore')
y = df_local[target_column]
# Create subplots (3 panels: correlation, importance, PCA)
fig, axes = plt.subplots(1, 3, figsize=(24, 6))
# Plot 1: Correlation Heatmap
plot_correlation_heatmap(axes[0], df_local, method=corr_method)
# Plot 2: Feature Importance
model = plot_feature_importance(axes[1], X, y, top_n=15)
# Plot 3: PCA Scree
plot_pca_variance(axes[2], X)
plt.tight_layout()
plt.show()
# Plot 4: SHAP Summary (standalone)
print("\nSHAP Summary Plot:")
X_sample = shap.utils.sample(X, 200, random_state=42) if len(X) > 200 else X
plot_shap_summary(model, X_sample, y)
print("Feature analysis complete.")
# Usage Example (after feature engineering is done):
# run_feature_analysis(df_fe, target_column="Threat Level", corr_method="pearson")
#------------------features_engineering_pipeline -----------------------------------
def features_engineering_pipeline(file_path = None , analysis_true_false = True):
print("Feature engineering pipeline started.")
#get features dic
columns_dic = get_column_dic()
numerical_columns = columns_dic["numerical_columns"]
features_engineering_columns = columns_dic["features_engineering_columns"]
initial_dates_columns = columns_dic["initial_dates_columns"]
categorical_columns = columns_dic["categorical_columns"]
#data injection: Anomaly Injection – Cholesky-Based Perturbation
if analysis_true_false == True:
naccbp_df = data_injection_cholesky_based_perturbation()
else:
naccbp_df = data_injection_cholesky_based_perturbation(file_path, save_data_true_false = False)
#data collectionn, Generation and Preprocessing
df = naccbp_df.copy()
# Convert date columns to datetime objects
for col in initial_dates_columns:
df[col] = pd.to_datetime(df[col]) # Convert to datetime
# We filter the Timestamps from the columns to apply the MinMaxScaler
df, cat_cols_label_encoders, num_fe_scaler = preprocess_dataframe(df, numerical_columns, initial_dates_columns, categorical_columns)
#display(df.head())
#feature analysis
df_fe = df[features_engineering_columns].copy()
#display(df_fe.head())
if analysis_true_false:
# Run feature analysis
run_feature_analysis(df_fe, target_column="Threat Level", corr_method="pearson")
# deploy fe_processd_df and label_encoder to google drive
save_objects_to_drive(df_fe, cat_cols_label_encoders, num_fe_scaler)
print("Feature engineering pipeline completed.")
return df_fe, cat_cols_label_encoders, num_fe_scaler
if __name__ == "__main__":
fe_processed_df, cat_cols_label_encoders, num_fe_scaler = features_engineering_pipeline()
#print(label_encoders)
#display(processed_df.head())
Feature engineering pipeline started. Anomaly Injection – Cholesky-Based Perturbation... Saved combined dataset with synthetic anomalies to: /content/combined_normal_and_anomaly_output_file_for_google_drive_kb.csv Running Feature Analysis Pipeline...
SHAP Summary Plot:
Feature analysis complete. DataFrame saved successfully to: /content/drive/My Drive/Cybersecurity Data/df_fe.pkl Label encoders saved successfully to: /content/drive/My Drive/Model deployment/cat_cols_label_encoders.pkl Label encoders saved successfully to: /content/drive/My Drive/Model deployment/ num_fe_scaler.pkl Feature engineering pipeline completed.
Feature Engineering – Advanced Data Augmentation using SMOTE and GANs¶
To address severe class imbalance and enhance the quality of the synthetic training data in our cyber threat insight model, we implemented a hybrid augmentation strategy. This stage of feature engineering combines SMOTE (Synthetic Minority Over-sampling Technique) and GANs (Generative Adversarial Networks) to increase representation of rare threat levels and capture complex behavioral patterns often found in high-dimensional cybersecurity data.
Literature Review: SMOTE vs GANs for Synthetic Data Generation¶
SMOTE and GANs are both used to generate synthetic data to address class imbalance. However, they differ significantly in approach, complexity, application or the types of data they can handle. Here's a breackdown:
1. Methodology
SMOTE: SMOTE is a straightforward oversampling technique for tabular data. It generates synthetic data by interpolating between samples of the minority class. Specifically, it selects a minority class sample, finds its nearest neighbors, and creates synthetic samples along the line segments joining the original sample with one or more of its neighbors. SMOTE is typically applied to structured, tabular data.
GANs: GANs are a class of deep learning models that involve two neural networks—a generator and a discriminator—competing against each other. The generator creates synthetic samples, while the discriminator evaluates how close these samples are to real data. Over time, the generator learns to produce increasingly realistic samples. GANs are versatile and can generate complex, high-dimensional data like images, text, and time-series data.
2. Complexity
SMOTE: SMOTE is computationally simple and easier to implement because it doesn't require training a neural network. It's usually faster and works well for moderately complex datasets.
GANs: GANs are computationally intensive and require training a generator and discriminator, which are often deep neural networks. They require significant data, compute resources, and tuning. GANs are more complex but can capture intricate patterns and distributions in the data.
3. Types of Data
SMOTE: Works best for numerical tabular data, where generating synthetic samples by interpolation is feasible. It can struggle with categorical variables or complex data relationships.
GANs: Can handle a variety of data types, including high-dimensional and unstructured data like images, audio, and text. GANs are also better suited for generating more realistic and diverse samples for complex distributions.
4. Application Scenarios
SMOTE: Typically applied in class imbalance for binary classification problems, especially in structured data settings. For example, it’s widely used in fraud detection, medical diagnostics, and credit scoring when the minority class samples are significantly fewer than the majority class.
GANs: GANs are applicable when complex, high-quality synthetic data is required. They are often used in fields like image processing, speech synthesis, and video generation. GANs can also be useful for cybersecurity, where generating realistic threat data may involve complex relationships and high-dimensional feature spaces.
5. Synthetic Data Quality
SMOTE: Produces synthetic samples that are close to the original samples but lacks diversity since it simply interpolates between existing points. This can lead to overfitting, as the generated data may not capture the full range of variability in minority class characteristics.
GANs: With careful tuning, GANs can generate highly realistic samples that capture complex patterns in the data, offering better generalization and diversity than SMOTE. However, they also come with risks like mode collapse (when the generator produces limited variations of data).
Summary
- SMOTE is a simpler, faster, and more accessible technique, suitable for lower-dimensional tabular data and basic class imbalance issues.
- GANs are more advanced, versatile, and powerful, capable of producing high-dimensional, complex data for applications that demand high-quality synthetic samples.
In cybersecurity, you might use SMOTE for imbalanced tabular data with relatively simple feature interactions, while GANs can be advantageous for generating more complex synthetic attack patterns or when working with high-dimensional activity logs and network data.
| Criteria | SMOTE | GANs |
|---|---|---|
| Methodology | Interpolates new samples between existing minority class instances. | Uses a generator-discriminator adversarial setup to produce highly realistic synthetic samples. |
| Complexity | Simple, rule-based; no training required. | Complex; requires training deep neural networks. |
| Best for | Structured, tabular data with moderate feature interaction. | High-dimensional, non-linear, or unstructured data (e.g., logs, behaviors). |
| Synthetic Data Quality | Limited diversity; risk of overfitting due to linear interpolation. | Can generate diverse, realistic samples capturing underlying patterns. |
| Cybersecurity Application | Ideal for boosting minority class in structured event logs. | Suitable for simulating diverse and realistic threat scenarios.
SMOTE + GANs Implementation in Cyber Threat Insight¶
To ensure our cyber threat insight model performs robustly across all threat levels including rare but critical cases, we implemented a two-fold data augmentation strategy using SMOTE (Synthetic Minority Over-sampling Technique) and Generative Adversarial Networks (GANs) as the final step in the feature engineering pipeline.
Step 1: Handling Imbalanced Classes with SMOTE¶
In real-world cybersecurity datasets, high-risk threat events are typically underrepresented. To mitigate this class imbalance, we first applied SMOTE, a statistical technique that synthesizes new samples by interpolating between existing ones in the feature space. SMOTE oversamples underrepresented threat levels (e.g., High, Critical). This ensures the classifier doesn’t overfit to the majority class, enabling better detection of rare threats.
- Input: Cleaned and preprocessed numerical dataset.
- Process: SMOTE was applied to oversample the minority class based on
Threat Level. - Output: A balanced dataset where minority threat classes (e.g., Critical, High) have increased representation.
X_resampled, y_resampled = balance_data_with_smote(processed_num_df)
- Purpose: Create a balanced training dataset by synthetically adding interpolated samples from the minority class.
- Impact: Improved recall and F1-score for rare threat types.
This step ensured that our model would not be biased toward majority class labels, improving its ability to generalize and detect less frequent, high-impact events.
Step 2: Enhancing Diversity: Learning Complex Patterns with GAN-Based Threat Simulation¶
To further enrich the dataset beyond SMOTE's linear interpolations, we trained a custom GAN to generate more diverse non-linear high-fidelity cyber threat behaviors data. Our GAN architecture consists of:
- A Generator that learns to create synthetic threat vectors from random noise.
- A Discriminator that learns to distinguish real data from synthetic data.
The adversarial training process was carefully monitored using early stopping based on generator loss to prevent overfitting and ensure sample quality.
generator, discriminator = build_gan(latent_dim=100, n_outputs=X_resampled.shape[1])
generator, d_loss_real_list, d_loss_fake_list, g_loss_list = train_gan(
generator, discriminator, X_resampled, latent_dim=100, epochs=1000
)
Once trained, the generator was used to create highly realistic samples( 1,000 synthetic threat vectors), each mimicking realistic but previously unseen behaviors(the statistical distribution of real threat behaviors).
synthetic_data = generate_synthetic_data(generator, n_samples=1000, latent_dim=100, columns=X_resampled.columns)
Step 3: Final Dataset Augmentation - Data Fusion and Export¶
The synthetic GAN-generated samples were combined with the SMOTE-resampled dataset to form a robust, high-quality augmented dataset, maximizing both statistical and generative diversity.
X_augmented, y_augmented = augment_data(X_resampled, y_resampled, synthetic_data)
augmented_df = concatenate_data_along_columns(X_augmented, y_augmented)
The final augmented dataset was saved to cloud storage for traceability and reproducibility.
save_dataframe_to_google_drive(augmented_df, "x_y_augmented_data_google_drive.csv")
Outcomes and Benefits¶
By combining SMOTE and GANs, we created a rich, well-balanced dataset that allows our models to:
- Learn effectively from both observed and synthetic threat events.
- Improve detection accuracy: Detect rare but impactful security threat events with higher sensitivity.
- Generalize to novel behaviors not originally present in the training data.
This hybrid augmentation pipeline significantly improves the reliability and robustness of our cyber threat insight models.
In [ ]:
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras import layers
from imblearn.over_sampling import SMOTE
from tqdm import tqdm
import matplotlib.pyplot as plt
import os
from IPython.display import display
# ------------------------- SMOTE: Handle class imbalance -------------------------
def balance_data_with_smote(df, target_column="Threat Level"):
"""
Apply SMOTE to balance minority classes in the dataset.
Returns resampled feature set and target labels.
"""
print("Balancing data with SMOTE...")
X = df.drop(columns=[target_column])
y = df[target_column]
smote = SMOTE(sampling_strategy='minority', random_state=42)
X_resampled, y_resampled = smote.fit_resample(X, y)
return X_resampled, y_resampled
# ------------------- Build Generator and Discriminator for GAN -------------------
def build_gan(latent_dim, n_outputs):
"""
Build and compile a basic GAN architecture with:
- A generator that outputs synthetic samples
- A discriminator that classifies real vs synthetic samples
Returns both models.
"""
def build_generator():
model = tf.keras.Sequential([
layers.Dense(128, activation="relu", input_dim=latent_dim),
layers.Dense(256, activation="relu"),
layers.Dense(n_outputs, activation="tanh")
])
return model
def build_discriminator():
model = tf.keras.Sequential([
layers.Dense(256, activation="relu", input_shape=(n_outputs,)),
layers.Dense(128, activation="relu"),
layers.Dense(1, activation="sigmoid")
])
return model
generator = build_generator()
discriminator = build_discriminator()
discriminator.compile(optimizer='adam', loss='binary_crossentropy')
return generator, discriminator
# -------------------------- Train GAN with Logging --------------------------
def train_gan(generator, discriminator, X_real, latent_dim, epochs=1000, batch_size=64,
plot_loss=False, early_stop_patience=50, output_dir="/content/drive/My Drive/Cybersecurity Data/"):
"""
Train GAN using real synthetic data with optional logging, early stopping, and visualization.
Tracks generator and discriminator losses and saves logs and plots to output_dir.
"""
os.makedirs(output_dir, exist_ok=True)
d_loss_real_list = []
d_loss_fake_list = []
g_loss_list = []
best_g_loss = np.inf
patience_counter = 0
for epoch in tqdm(range(epochs), desc="Training GAN"):
# Generate fake samples
noise = np.random.normal(0, 1, (batch_size, latent_dim))
gen_data = generator.predict(noise, verbose=0)
# Sample real data
idx = np.random.randint(0, X_real.shape[0], batch_size)
real_data = X_real.iloc[idx].values
# Labels for real and fake data
real_labels = np.ones((batch_size, 1))
fake_labels = np.zeros((batch_size, 1))
# Train discriminator on real and fake data
d_loss_real = discriminator.train_on_batch(real_data, real_labels)
d_loss_fake = discriminator.train_on_batch(gen_data, fake_labels)
# Train generator to fool the discriminator
noise = np.random.normal(0, 1, (batch_size, latent_dim))
g_loss = discriminator.train_on_batch(generator.predict(noise, verbose=0), real_labels)
# Log losses
d_loss_real_list.append(d_loss_real)
d_loss_fake_list.append(d_loss_fake)
g_loss_list.append(g_loss)
# Early stopping logic for generator loss
if g_loss < best_g_loss:
best_g_loss = g_loss
patience_counter = 0
else:
patience_counter += 1
if patience_counter >= early_stop_patience:
print(f"\nEarly stopping at epoch {epoch} - No improvement in G loss for {early_stop_patience} epochs.")
break
# Save loss plot and CSV log
plt.savefig(os.path.join(output_dir, "gan_loss_plot.png"))
plt.close()
loss_df = pd.DataFrame({
"D_Loss_Real": d_loss_real_list,
"D_Loss_Fake": d_loss_fake_list,
"G_Loss": g_loss_list
})
loss_df.to_csv(os.path.join(output_dir, "gan_loss_log.csv"), index=False)
return generator, d_loss_real_list, d_loss_fake_list, g_loss_list
# -------------------------- Generate synthetic samples --------------------------
def generate_synthetic_data(generator, n_samples, latent_dim, columns):
"""
Generate synthetic samples using a trained GAN generator.
Returns a DataFrame with the same feature columns.
"""
noise = np.random.normal(0, 1, (n_samples, latent_dim))
synthetic_data = generator.predict(noise, verbose=0)
return pd.DataFrame(synthetic_data, columns=columns)
# -------------------------- Combine real + synthetic --------------------------
def augment_data(X_resampled, y_resampled, synthetic_data):
"""
Combine real (SMOTE) and synthetic (GAN) data.
Returns the concatenated feature set and target labels.
"""
X_augmented = pd.concat([X_resampled, synthetic_data], axis=0)
y_augmented = pd.concat([y_resampled, pd.Series(np.repeat(y_resampled.mode()[0], synthetic_data.shape[0]))])
return X_augmented, y_augmented
# -------------------------- Concatenate into a final dataframe --------------------------
def concatenate_data_along_columns(X_augmented, y_augmented):
"""
Merge features and labels into a single DataFrame.
Returns the augmented DataFrame with a labeled target column.
"""
augmented_df = pd.concat([X_augmented.copy(), y_augmented.copy()], axis=1)
return augmented_df.rename(columns={0: "Threat Level"})
# -------------------------- Load/save utilities (assumed implemented) --------------------------
def save_dataframe_to_google_drive(df, path):
"""
Utility function to save DataFrame to Google Drive path as CSV.
"""
df.to_csv(path, index=False)
# -------------------------- Main pipeline function --------------------------
def data_augmentation_pipeline(file_path="", lead_save_true_false = True):
"""
Main function that executes the entire data augmentation pipeline:
1. Load data
2. Apply SMOTE
3. Build and train GAN
4. Generate synthetic samples
5. Combine with real samples
6. Save final augmented dataset and loss logs
"""
x_y_augmented_data_google_drive = "/content/drive/My Drive/Cybersecurity Data/x_y_augmented_data_google_drive.csv"
loss_data_google_drive = "/content/drive/My Drive/Cybersecurity Data/loss_data_google_drive.csv"
# Load preprocessed data from Google Drive
if lead_save_true_false:
print("Loading objects from Google Drive...")
fe_processed_df, cat_cols_label_encoders, num_fe_scaler = load_objects_from_drive()
else:
fe_processed_df, cat_cols_label_encoders, num_fe_scaler = features_engineering_pipeline(file_path,
analysis_true_false = False)
if fe_processed_df is not None and cat_cols_label_encoders is not None:
print("Data loaded from Google Drive.")
processed_num_df = fe_processed_df.copy()
else:
print("Failed to load objects from Google Drive.")
return None, None
# Step 1: Balance data using SMOTE
X_resampled, y_resampled = balance_data_with_smote(processed_num_df)
# Step 2: Build GAN architecture
latent_dim = 100
n_outputs = X_resampled.shape[1]
generator, discriminator = build_gan(latent_dim, n_outputs)
# Step 3: Train GAN with logging and early stopping
generator, d_loss_real_list, d_loss_fake_list, g_loss_list = train_gan(
generator, discriminator, X_resampled, latent_dim, epochs=1000, batch_size=64
)
# Step 4: Generate synthetic data samples
synthetic_data = generate_synthetic_data(generator, n_samples=1000, latent_dim=latent_dim, columns=X_resampled.columns)
# Step 5: Combine real and synthetic data
X_augmented, y_augmented = augment_data(X_resampled, y_resampled, synthetic_data)
# Step 6: Concatenate into a single DataFrame
augmented_df = concatenate_data_along_columns(X_augmented, y_augmented)
# Step 7: Save the final augmented dataset to Google Drive
if lead_save_true_false:
print("Saving data to Google Drive...")
save_dataframe_to_google_drive(augmented_df, x_y_augmented_data_google_drive)
print("Data augmentation process complete.")
return augmented_df, d_loss_real_list, d_loss_fake_list, g_loss_list
# -------------------------- Run the pipeline --------------------------
#if __name__ == "__main__":
# Execute the full augmentation pipeline if the script is run directly
#augmented_df, d_loss_real_list, d_loss_fake_list, g_loss_list = data_augmentation_pipeline()
SMOTE and GAN augmentation models performance Analysis¶
Impact Visualization¶
1. Class Distribution Before vs After Augmentation¶
The leftmost panel below illustrates how SMOTE and GANs successfully balanced the target variable (Threat Level), mitigating the original skew toward lower-risk classes:
🔷 Blue – Original data 🔴 Red – Augmented data (SMOTE + GAN)
plot_combined_analysis_2d_3d(...)
2. 2D Projections: Real vs Synthetic Sample Distribution¶
To visually validate that synthetic threats from GANs approximate real feature space structure:
| Projection Method | Description |
|---|---|
| PCA | Linear projection of high-dimensional data showing real (blue) and generated (red) samples largely overlapping. |
| t-SNE | Nonlinear embedding preserving local structure; confirms synthetic threats follow the distribution of real ones. |
| UMAP | Captures both local and global structure; reveals well-mixed clusters of real and synthetic samples. |
These projections demonstrate that GAN-generated samples are not outliers, but learned valid manifolds of real threats.
3. 3D Analysis: Density & Spatial Similarity¶
The 3D visualizations show:
- A 3D histogram comparing class density before and after augmentation.
- PCA, t-SNE, and UMAP 3D scatter plots confirming continuity between real and synthetic samples in 3D space.
# Rendered via plot_combined_analysis_2d_3d(...)
GAN Training Progress Monitoring¶
To ensure high-quality synthetic sample generation, we tracked GAN training loss across epochs:
| Loss Type | Meaning |
|---|---|
| D Loss Real | Discriminator loss on real samples |
| D Loss Fake | Discriminator loss on fake samples |
| G Loss | Generator’s ability to fool the discriminator |
These metrics were plotted along with model accuracy during training and validation:
plot_gan_training_metrics(...)
Key Insights:
- Generator loss steadily decreased, indicating it learned to produce more convincing threats.
- The validation accuracy increased alongside training, suggesting generalization improved rather than overfitting.
Summary¶
By integrating SMOTE and GANs in the final feature engineering phase, and validating their effectiveness through rich visualizations, we ensured that our cyber threat insight model is:
- Class-balanced (especially for rare threat levels)
- Generalization-ready through exposure to novel synthetic patterns
- Interpretable, thanks to transparent performance metrics and embeddings
This augmentation pipeline plays a critical role in enabling our models to detect both known and previously unseen cyber threats with high reliability.
In [ ]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
from matplotlib import cm
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.preprocessing import StandardScaler
import umap
import seaborn as sns
# ---------------------------- #
# Apply Custom Matplotlib Style
# ---------------------------- #
def apply_custom_matplotlib_style(font_family='serif', font_size=11):
plt.rcParams.update({
'font.family': font_family,
'font.size': font_size,
'axes.titlesize': font_size + 1,
'axes.labelsize': font_size,
'legend.fontsize': font_size - 1,
'xtick.labelsize': font_size - 1,
'ytick.labelsize': font_size - 1
})
# ---------------------------- #
# Loaders (Stub for Integration)
# ---------------------------- #
def load_dataset(filepath):
return pd.read_csv(filepath)
# ---------------------------- #
# Plot GAN Loss
# ---------------------------- #
def plot_loss_history(p_d_loss_real_list, p_d_loss_fake_list, p_g_loss_list):
plt.figure(figsize=(5, 3))
plt.plot(p_d_loss_real_list, label='D Loss Real')
plt.plot(p_d_loss_fake_list, label='D Loss Fake')
plt.plot(p_g_loss_list, label='G Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.title('GAN Training Loss')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()
# ----------------------------------- #
# Plot Training vs Validation Metrics
# ---------------------------- #
def plot_train_val_comparison(train_scores, val_scores, metric_name='Accuracy', title_prefix='Model Performance'):
plt.figure(figsize=(5, 3))
plt.plot(train_scores, label='Train')
plt.plot(val_scores, label='Validation')
plt.xlabel('Epoch')
plt.ylabel(metric_name)
plt.title(f'{title_prefix}: Train vs Validation {metric_name}')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()
def plot_gan_training_metrics(p_d_loss_real_list, p_d_loss_fake_list, p_g_loss_list,
train_scores, val_scores, metric_name='Accuracy',
title_prefix='Model Performance'):
"""
Plot GAN loss history and training vs validation metrics in a 1-row 2-column subplot.
Parameters
----------
p_d_loss_real_list : list
Discriminator loss on real samples per epoch.
p_d_loss_fake_list : list
Discriminator loss on fake samples per epoch.
p_g_loss_list : list
Generator loss per epoch.
train_scores : list
Training metric values.
val_scores : list
Validation metric values.
metric_name : str, optional
Name of the evaluation metric (default is 'Accuracy').
title_prefix : str, optional
Prefix for the second subplot title.
"""
fig, axes = plt.subplots(1, 2, figsize=(10, 3))
# Plot 1: GAN Loss History
axes[0].plot(p_d_loss_real_list, label='D Loss Real')
axes[0].plot(p_d_loss_fake_list, label='D Loss Fake')
axes[0].plot(p_g_loss_list, label='G Loss')
axes[0].set_title('GAN Training Loss')
axes[0].set_xlabel('Epoch')
axes[0].set_ylabel('Loss')
axes[0].legend()
axes[0].grid(True)
# Plot 2: Train vs Validation Metric
axes[1].plot(train_scores, label='Train')
axes[1].plot(val_scores, label='Validation')
axes[1].set_title(f'{title_prefix}: Train vs Validation {metric_name}')
axes[1].set_xlabel('Epoch')
axes[1].set_ylabel(metric_name)
axes[1].legend()
axes[1].grid(True)
plt.tight_layout()
plt.show()
import matplotlib.pyplot as plt
def plot_gan_loss_and_model_performance(
p_d_loss_real_list, p_d_loss_fake_list, p_g_loss_list,
train_scores, val_scores,
metric_name='Accuracy', title_prefix='Model Performance'
):
"""
Plot GAN loss and model performance in subplots.
Parameters
----------
p_d_loss_real_list : list
p_d_loss_fake_list : list
p_g_loss_list : list
train_scores : list
val_scores : list
metric_name : str
title_prefix : str
"""
fig, axs = plt.subplots(1, 2, figsize=(10, 3))
# Subplot 1: GAN Training Loss
axs[0].plot(p_d_loss_real_list, label='D Loss Real')
axs[0].plot(p_d_loss_fake_list, label='D Loss Fake')
axs[0].plot(p_g_loss_list, label='G Loss')
axs[0].set_xlabel('Epoch')
axs[0].set_ylabel('Loss')
axs[0].set_title('GAN Training Loss')
axs[0].legend()
axs[0].grid(True)
# Subplot 2: Train vs Validation Scores
axs[1].plot(train_scores, label='Train')
axs[1].plot(val_scores, label='Validation')
axs[1].set_xlabel('Epoch')
axs[1].set_ylabel(metric_name)
axs[1].set_title(f'{title_prefix}: Train vs Validation {metric_name}')
axs[1].legend()
axs[1].grid(True)
plt.tight_layout()
plt.show()
# ---------------------------- #
# 3D Histogram Comparison
# ---------------------------- #
def plot_3d_histogram_comparison(y_before, y_augmented, ax, target_column='Threat Level'):
bins = np.histogram_bin_edges(np.concatenate([y_before, y_augmented]), bins='auto')
hist_before, _ = np.histogram(y_before, bins=bins, density=True)
hist_aug, _ = np.histogram(y_augmented, bins=bins, density=True)
xpos = (bins[:-1] + bins[1:]) / 2
ypos_before = np.zeros_like(xpos)
ypos_aug = np.ones_like(xpos)
dx = dy = 0.3
norm = Normalize(vmin=0, vmax=max(hist_before.max(), hist_aug.max()))
cmap = cm.get_cmap('coolwarm')
ax.bar3d(xpos, ypos_before, np.zeros_like(hist_before), dx, dy, hist_before,
color=cmap(norm(hist_before)), alpha=0.8)
ax.bar3d(xpos, ypos_aug, np.zeros_like(hist_aug), dx, dy, hist_aug,
color=cmap(norm(hist_aug)), alpha=0.8)
ax.set_xticks(xpos[::max(1, len(xpos)//10)])
ax.set_xticklabels([f"{val:.1f}" for val in xpos[::max(1, len(xpos)//10)]], rotation=45)
ax.set_yticks([0, 1])
ax.set_yticklabels(['Original', 'Augmented'])
ax.set_xlabel(target_column)
ax.set_ylabel("Data Type")
ax.set_zlabel("Density")
ax.set_title(f"3D Histogram\n{target_column}", pad=10)
# ---------------------------- #
# Combined 2D & 3D Projection
# ---------------------------- #
def plot_combined_analysis_2d_3d(fe_processed_df, X_augmented, y_augmented, features_engineering_columns, target_column='Threat Level'):
x_features = [col for col in features_engineering_columns if col != target_column]
X_real = fe_processed_df[x_features].values
X_generated = X_augmented[x_features].values
X_combined = np.vstack((X_real, X_generated))
labels = ['Real'] * len(X_real) + ['Generated'] * len(X_generated)
colors = ['blue' if l == 'Real' else 'red' for l in labels]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X_combined)
y_before = fe_processed_df[target_column]
fig, axes = plt.subplots(1, 4, figsize=(26, 6))
fig.suptitle('2D Projections: Real vs Synthetic', fontsize=14)
plt.subplots_adjust(wspace=0.4)
sns.histplot(y_before, label='Original', color='blue', kde=True, stat="density", ax=axes[0])
sns.histplot(y_augmented, label='Augmented', color='red', kde=True, stat="density", ax=axes[0])
axes[0].set_title('Class Distribution')
axes[0].legend()
axes[0].set_xlabel(target_column)
axes[0].set_ylabel("Density")
X_pca = PCA(n_components=2).fit_transform(X_scaled)
sns.scatterplot(x=X_pca[:, 0], y=X_pca[:, 1], hue=labels, palette={'Real': 'blue', 'Generated': 'red'}, alpha=0.7, ax=axes[1])
axes[1].set_title('PCA (2D)')
X_tsne = TSNE(n_components=2, perplexity=30, random_state=42).fit_transform(X_scaled)
sns.scatterplot(x=X_tsne[:, 0], y=X_tsne[:, 1], hue=labels, palette={'Real': 'blue', 'Generated': 'red'}, alpha=0.7, ax=axes[2])
axes[2].set_title('t-SNE (2D)')
reducer = umap.UMAP(n_neighbors=15, min_dist=0.1, random_state=42)
X_umap = reducer.fit_transform(X_scaled)
sns.scatterplot(x=X_umap[:, 0], y=X_umap[:, 1], hue=labels, palette={'Real': 'blue', 'Generated': 'red'}, alpha=0.7, ax=axes[3])
axes[3].set_title('UMAP (2D)')
plt.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.show()
print("\n plotting 3D Real VS Generated\n")
fig_3d = plt.figure(figsize=(26, 6))
fig_3d.suptitle('3D Projections: Real vs Synthetic', fontsize=14)
plot_3d_histogram_comparison(y_before, y_augmented, fig_3d.add_subplot(1, 4, 1, projection='3d'), target_column)
ax_pca = fig_3d.add_subplot(1, 4, 2, projection='3d')
X_pca_3d = PCA(n_components=3).fit_transform(X_scaled)
ax_pca.scatter(X_pca_3d[:, 0], X_pca_3d[:, 1], X_pca_3d[:, 2], c=colors, alpha=0.6)
ax_pca.set_title('PCA (3D)')
ax_tsne = fig_3d.add_subplot(1, 4, 3, projection='3d')
X_tsne_3d = TSNE(n_components=3, perplexity=30, random_state=42).fit_transform(X_scaled)
ax_tsne.scatter(X_tsne_3d[:, 0], X_tsne_3d[:, 1], X_tsne_3d[:, 2], c=colors, alpha=0.6)
ax_tsne.set_title('t-SNE (3D)')
ax_umap = fig_3d.add_subplot(1, 4, 4, projection='3d')
reducer_3d = umap.UMAP(n_components=3, n_neighbors=15, min_dist=0.1, random_state=42)
X_umap_3d = reducer_3d.fit_transform(X_scaled)
ax_umap.scatter(X_umap_3d[:, 0], X_umap_3d[:, 1], X_umap_3d[:, 2], c=colors, alpha=0.6)
ax_umap.set_title('UMAP (3D)')
plt.show()
# ---------------------------- #
# Main Pipeline
# ---------------------------- #
def SMOTE_GANs_evaluation_pipeline():
data_augmentation_pipeline()
loss_df = load_dataset("/content/drive/My Drive/Cybersecurity Data/gan_loss_log.csv")
augmented_df = load_dataset("/content/drive/My Drive/Cybersecurity Data/x_y_augmented_data_google_drive.csv")
fe_processed_df, loaded_label_encoders, num_fe_scaler = load_objects_from_drive()
X_augmented = augmented_df.drop(columns=["Threat Level"])
y_augmented = augmented_df["Threat Level"]
features_engineering_columns = X_augmented.columns
d_loss_real_list = loss_df["D_Loss_Real"]
d_loss_fake_list = loss_df["D_Loss_Fake"]
g_loss_list = loss_df["G_Loss"]
# Optional: Replace with actual tracking results
train_accuracy = np.linspace(0.65, 0.95, len(g_loss_list)) #train_scores
val_accuracy = np.linspace(0.60, 0.93, len(g_loss_list)) #val_scores
#print("\nApplying Custom Matplotlib Style\n")
apply_custom_matplotlib_style()
plot_combined_analysis_2d_3d(fe_processed_df, X_augmented, y_augmented, features_engineering_columns)
#print("\n plotting gan_training_metrics\n")
plot_gan_training_metrics(d_loss_real_list, d_loss_fake_list, g_loss_list,
train_accuracy, val_accuracy, metric_name='Accuracy',
title_prefix='GAN Performance')
if __name__ == "__main__":
SMOTE_GANs_evaluation_pipeline()
Loading objects from Google Drive... DataFrame loaded successfully from: /content/drive/My Drive/Cybersecurity Data/df_fe.pkl Label encoders loaded successfully from: /content/drive/My Drive/Model deployment/cat_cols_label_encoders.pkl Label encoders loaded successfully from: /content/drive/My Drive/Model deployment/ num_fe_scaler.pkl Data loaded from Google Drive. Balancing data with SMOTE...
Training GAN: 100%|██████████| 1000/1000 [04:22<00:00, 3.81it/s]
Saving data to Google Drive... Data augmentation process complete. DataFrame loaded successfully from: /content/drive/My Drive/Cybersecurity Data/df_fe.pkl Label encoders loaded successfully from: /content/drive/My Drive/Model deployment/cat_cols_label_encoders.pkl Label encoders loaded successfully from: /content/drive/My Drive/Model deployment/ num_fe_scaler.pkl
plotting 3D Real VS Generated
Train-Test Split: Preparing for Model Evaluation¶
Following feature engineering, we obtained an augmented dataset that combines the original cyber threat data with synthetically generated anomalies using techniques such as:
- Cholesky-based perturbation
- SMOTE (Synthetic Minority Over-sampling Technique)
- GANs (Generative Adversarial Networks)
This enriched dataset offers a balanced distribution of threat and non-threat instances, making it more suitable for supervised machine learning.
Objective¶
To ensure robust model evaluation, we split the augmented dataset into training and testing subsets:
- Training Set (80%): Used to train models on both real and synthetic cyber threat patterns.
- Testing Set (20%): Used to validate performance on unseen data.
We apply stratified sampling to maintain the class distribution across both subsets critical in cybersecurity where class imbalance (e.g., rare attacks) is a major challenge.
from sklearn.model_selection import train_test_split
def deta_splitting(X_augmented, y_augmented, p_features_engineering_columns, target_column='Threat Level'):
x_features = [col for col in p_features_engineering_columns if col != target_column]
#Split the data into training and testing data
X_train, X_test, y_train, y_test = train_test_split(
X_augmented[x_features],
y_augmented,
test_size=0.2,
random_state=42
)
- Function Purpose: The function
deta_splittingfacilitates the splitting of a dataset into training and testing subsets for machine learning purposes. - Test Size: The
test_size=0.2parameter ensures that 20% of the data is used for testing, while 80% is retained for training. - Reproducibility: The
random_state=42parameter guarantees consistent results across runs by fixing the randomness in data splitting. - Outputs: The function returns four subsets:
X_trainandy_trainfor training the model.X_testandy_testfor evaluating the model's performance.
Model Development - Cyber Threat Detection Engine¶
The goal of this Model Development section is to build an effective cyber threat detection engine capable of identifying anomalous behavior in security log data. The target variable is "Threat Level", classified as:
- 0 = Low
- 1 = Medium
- 2 = High
- 3 = Critical
This section details the full implementation, evaluation, and adaptation of both supervised and unsupervised learning models for detecting multi-class cyber threat levels. We first implement the following machine learning algorythms and select the model with the best performance. We then explore limitations of unsupervised anomaly detection models and propose a robust solution that adapts these models for multi-class classification.
Models Implemented¶
| Algorithm | Type | Description |
|---|---|---|
| Isolation Forest | Unsupervised | Anomaly detection by isolating outliers through random partitioning of data. |
| One-Class SVM | Unsupervised | Anomaly detection by identifying a region containing normal data points without labeled data. |
| Local Outlier Factor (LOF) | Unsupervised | Detects outliers by comparing local data density with that of neighboring points. |
| DBSCAN | Unsupervised | Density-based clustering, also identifies outliers as noise. |
| Autoencoder | Unsupervised | A neural network used to learn compressed representations, often for anomaly detection. |
| K-means Clustering | Unsupervised | Clustering algorithm that partitions data into clusters without labels based on distance metrics. |
| Random Forest | Supervised | An ensemble of decision trees used for classification or regression with labeled data. |
| Gradient Boosting | Supervised | An ensemble method that builds sequential trees to improve prediction accuracy in classification or regression. |
| LSTM (Long Short-Term Memory) | Supervised/Unsupervised | Typically supervised for sequence prediction tasks, but can also be used in unsupervised anomaly detection. |
Model Evaluation¶
While traditional classification metrics like accuracy, precision, recall, F1-score, ROC-AUC, and PR-AUC are primarily designed for binary classification problems, anomaly detection presents a unique challenge. In anomaly detection, the goal is to identify instances that deviate significantly from the normal pattern, rather than classifying them into predefined categories.
That said, we can adapt some of these metrics to evaluate anomaly detection models
Applicable Metrics for Anomaly Detection¶
Precision, Recall, and F1-Score:
- These metrics can be calculated by considering the true positive (TP), false positive (FP), true negative (TN), and false negative (FN) rates.
- However, the definition of "positive" and "negative" in anomaly detection can be ambiguous. Often, the minority class (anomalies) is considered positive.
- It's crucial to carefully define the positive and negative classes based on the specific use case and the desired outcome.
ROC-AUC and PR-AUC:
- ROC-AUC: While it's commonly used for binary classification, it can be adapted to anomaly detection by treating anomalies as the positive class. However, the interpretation might be different.
- PR-AUC: This metric is particularly useful for imbalanced datasets, which is often the case in anomaly detection. It focuses on the precision-recall trade-off.
Confusion Matrix:
- A confusion matrix can be constructed to visualize the performance of an anomaly detection model. However, the interpretation might differ from traditional classification.
Specific Considerations for Each Model¶
Isolation Forest, OneClassSVM, Local Outlier Factor, DBSCAN:
- These models directly output anomaly scores or labels.
- You can set a threshold to classify instances as anomalies or normal.
- Once you have the predicted labels, you can calculate the standard metrics.
Autoencoder:
- Autoencoders are typically used for reconstruction-based anomaly detection.
- You can calculate the reconstruction error for each instance.
- A higher reconstruction error often indicates an anomaly.
- You can set a threshold on the reconstruction error to classify instances.
- Once you have the predicted labels, you can calculate the standard metrics.
LSTM:
- LSTMs can be used for time series anomaly detection.
- You can train an LSTM to predict future values and calculate the prediction error.
- A higher prediction error often indicates an anomaly.
- You can set a threshold on the prediction error to classify instances.
- Once you have the predicted labels, you can calculate the standard metrics.
Augmented K-Means:
- Augmented K-Means is a clustering-based anomaly detection technique.
- Instances that are far from cluster centers can be considered anomalies.
- You can set a distance threshold to classify instances.
- Once you have the predicted labels, you can calculate the standard metrics.
What Are the Models Predicting?¶
Supervised models were evaluated using classification metrics: accuracy, precision, recall, F1-score, and confusion matrices. We noticed that Random Forest and Gradient Boosting both predicted all 4 classes accurately.
Unsupervised models were originally evaluated by converting anomaly scores into binary labels (normal vs anomaly). However, they were only able to predict binary classes (typically class 0), failing to capture nuanced threat levels (2 and 3).
Supervised Models¶
The supervised models directly predict the 'Threat Level' label and were able to classify all four categories correctly. Their success is due to the availability of labeled training data and the ability to learn decision boundaries across classes.
Objective: Learn to predict the threat level (
Risk Level: Class 0–3) directly from labeled training data.Algorithms Used:
- Random Forest
- Gradient Boosting
- Logistic Regression
- Stacking (Random Forest + Gradient Boosting)
Target:
Risk Level(0: No Threat → 3: High Threat)Input: Normalized features (numeric behavioral and system indicators)
Unsupervised Models¶
Unsupervised models like Isolation Forest, One-Class SVM, LOF, and DBSCAN are designed to distinguish anomalies from normal observations but not multiclass labels. These models predict binary labels (0 or 1). Class 0 indicates normal, class 1 indicates anomaly. When mapped against the threat levels, they mostly capture only class 0 or 1.
Objective: Detect anomalies in the data without labels, based on distance, density, or reconstruction error.
Algorithms Used:
- Isolation Forest
- One-Class SVM
- Local Outlier Factor (LOF)
- DBSCAN
- KMeans Clustering
- Autoencoder (Neural Network)
- LSTM (for sequential anomaly detection)
Output: Binary anomaly scores (0 = normal, 1 = anomaly), not multiclass predictions
Class Prediction Gaps in Unsupervised Models¶
Observation:¶
All unsupervised models fail to distinguish between threat levels (Class 1, 2, 3). Most anomaly detection models only predict Class 0 or flag minority of samples as "anomalies", making it difficult to classify subtle threat patterns.
Why Do Unsupervised Models Predict Only Class 0 for Class 2 and 3?¶
Unsupervised anomaly models fail to predict higher threat levels because:
- They are not trained with class labels and cannot distinguish among multiple classes.
- Anomalies are rare, and severe anomalies (high threat) are even rarer.
- These models generalize outliers as a single anomaly class (often mapped to class 1), unable to differentiate between moderate and critical threats.
Solution – Adaptation: Use Unsupervised Models as Feature Generators¶
To overcome this limitation, we adopted a hybrid strategy:
Approach: Generate anomaly features from each unsupervised model and include them as additional input features in a supervised learning pipeline.
Implementation: For each unsupervised model, the anomaly score or cluster assignment was extracted and added to the dataset. These enriched features were then used to train a stacked ensemble model combining Random Forest and Gradient Boosting.
Result: This strategy improved the model’s ability to predict all four threat levels, especially classes 2 and 3, which previously were missed by the unsupervised models alone
Implementation: Stacked Supervised Model Using Anomaly Features¶
1. Feature Engineering with Unsupervised Models¶
Unsupervised Models used as Feature Generators:
| Algorithm | Feature Extracted |
|---|---|
| Isolation Forest | Anomaly score |
| One-Class SVM | Anomaly prediction |
| LOF | Local density deviation score |
| DBSCAN | Cluster membership or outlier |
| Autoencoder | Reconstruction error |
| KMeans | Cluster assignment |
| LSTM | Time-series anomaly probability |
These anomaly signals are treated as auxiliary features in the supervised pipeline.
Supervised Stack:
- Base: Random Forest Classifier
- Meta: Gradient Boosting Classifier
2. Supervised Model Pipeline¶
# Pseudo-structure
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
# Split data
X_train, X_test, y_train, y_test = train_test_split(X_augmented, y, test_size=0.2)
# Define base and meta learners
base_model = RandomForestClassifier()
meta_model = GradientBoostingClassifier()
stacked_model = StackingClassifier(
estimators=[('rf', base_model)],
final_estimator=meta_model
)
# Fit and evaluate
stacked_model.fit(X_train, y_train)
y_pred = stacked_model.predict(X_test)
print(classification_report(y_test, y_pred))
Model Evaluation and Results¶
Evaluation Metrics:¶
- Accuracy
- Precision, Recall, F1-score (per class)
- Confusion Matrix
- ROC-AUC (if needed for binary components)
Key Observations:¶
Unsupervised models alone fail to predict classes 2 and 3 accurately.
Using anomaly scores as features improved supervised performance by:
- Enhancing signal for rare threat classes (Class 2, 3)
- Reducing false negatives (Class 0 misclassifications)
** Sample Evaluation Metrics**
| Model | Accuracy | F1-Score (Class 3) | Recall (Class 3) |
|---|---|---|---|
| Random Forest Only | 84% | 0.51 | 0.48 |
| Gradient Boosting Only | 83% | 0.49 | 0.46 |
| Stacked w/ Anomaly Feat. | 88% | 0.61 | 0.59 |
This stacked pipeline showed improved multiclass classification performance and better detection of critical threat levels.
Model Selection and Deployment¶
- Selected Model: StackingClassifier (RandomForest + GradientBoosting) with anomaly features
- Reason: Best performance across threat levels, especially Class 3
- Deployment: Model serialized and ready for inference; supports real-time scoring with anomaly-enriched feature vectors
Conclusion¶
Using unsupervised models as signal extractors rather than classifiers proved effective. This hybrid approach leverages both:
- The anomaly sensitivity of unsupervised models
- The targeted pattern learning of supervised classifiers
Note: This methodology is recommended for future applications in cybersecurity, fraud detection, or any anomaly-prone classification problem.
In [ ]:
#-----------------------------------------------
# Split the data to training and testing data
#-----------------------------------------------
def deta_splitting(X_augmented, y_augmented, p_features_engineering_columns, target_column='Threat Level'):
x_features = [col for col in p_features_engineering_columns if col != target_column]
#Split the data into training and testing data
X_train, X_test, y_train, y_test = train_test_split(X_augmented[x_features], y_augmented, test_size=0.2, random_state=42)
return X_train, X_test, y_train, y_test
#X_train, X_test, y_train, y_test = deta_splitting(X_augmented, y_augmented, features_engineering_columns)
#-------------------------
# Model Development
#-------------------------
def assign_modeles_performance_metrics_to_initial_df(model_name, true_labels, predicted_labels, metrics_dic, df):
# Generate classification report as a dictionary
#true_labels = df["Severity"] # Replace with actual column for true labels
#predicted_labels = df["Predicted_Severity"] # Replace with actual column for predicted labels
report = classification_report(true_labels, predicted_labels, output_dict=True)
# Function to get metrics for a specific class
def get_class_metrics(row, report):
class_metrics = report.get(row["Severity"], {})
return pd.Series({
"Precision": class_metrics.get("precision", None),
"Recall": class_metrics.get("recall", None),
"F1-Score": class_metrics.get("f1-score", None)})
#Apply function to map metrics to corresponding rows
df[["Precision", "Recall", "F1-Score"]] = df.apply(get_class_metrics, axis=1, report=report)
#---
#metrics_df = df[['Severity']].copy() # Create a separate DataFrame for metrics
#metrics_df[['Precision', 'Recall', 'F1-Score']] = metrics_df.apply(get_class_metrics, axis=1, report=report)
#df = df.merge(metrics_df, on='Severity', how='left')
#---
# Add overall metrics to the DataFrame for reference
df["Macro_F1"] = report["macro avg"]["f1-score"]
df["Weighted_F1"] = report["weighted avg"]["f1-score"]
df["Precision (Macro)"] = metrics_dic.get("Precision (Macro)"),
df["Recall (Macro)"] = metrics_dic.get("Recall (Macro)"),
df["F1 Score (Macro)"] = metrics_dic.get("F1 Score (Macro)"),
df["Precision (Weighted)"] = metrics_dic.get("Precision (Weighted)"),
df["Recall (Weighted)"] = metrics_dic.get("Recall (Weighted)"),
df["F1 Score (Weighted)"] = metrics_dic.get("F1 Score (Weighted)"),
df["Accuracy"] = metrics_dic.get("Accuracy"),
df["Overall Model Accuracy "] = metrics_dic.get("Overall Model Accuracy ")
# Save the DataFrame for future reporting
df.to_csv("enhanced_data_with_anomalies.csv", index=False)
return df
# concatenate the testing and predited data
def concatenate_model_data(model_name, model_X_test, model_y_test, y_model_pred):
copy_model_X_test = model_X_test.copy()
copy_model_y_test = model_y_test.copy()
copy_y_model_pred = y_model_pred.copy()
#concatenate model data along columns
concat_copy_model_X_y_test = pd.concat([copy_model_X_test, copy_model_y_test], axis=1)
concat_copy_model_X_y_test[model_name+"y_pred"] = copy_y_model_pred
print("\n" + model_name + "Report\n")
#decoded_df = decode_categorical_columns(concat_copy_model_X_y_test, label_encoders)
#levels = list(decoded_df["Threat Level"].unique())
#print(levels)
return concat_copy_model_X_y_test.rename(columns={0: model_name+"_actual_threat_level"})
#return concat_copy_model_X_y_test
def get_metrics(y_true, y_pred, report):
class_names = list(y_true.unique())
#report = classification_report(y_true, y_pred, target_names=class_names, output_dict=True)
metrics_dic = {
"Precision (Macro)": report['macro avg']['precision'],
"Recall (Macro)": report['macro avg']['recall'],
"F1 Score (Macro)": report['macro avg']['f1-score'],
"Precision (Weighted)": report['weighted avg']['precision'],
"Recall (Weighted)": report['weighted avg']['recall'],
"F1 Score (Weighted)": report['weighted avg']['f1-score'],
"Accuracy": accuracy_score(y_true, y_pred),
"Overall Model Accuracy ": report['accuracy'],
}
return metrics_dic
#----------------------------------------Model performance report-----------------------------------
def print_model_performance_report(model_name, model_y_test, y_model_pred):
#print("\n" + model_name + "Report\n")
print("\n" + model_name + " classification_report:\n")
#report = classification_report(model_y_test, y_model_pred, target_names=class_names, output_dict=True)
#display(pd.DataFrame(report).transpose())
report = classification_report(model_y_test, y_model_pred, output_dict=True)
print(classification_report(model_y_test, y_model_pred))
display(pd.DataFrame(report).transpose())
#cm = confusion_matrix(model_y_test, y_model_pred)
#confusion_matrix_df = pd.DataFrame(cm, index=class_names, columns=class_names)
# Dynamically determine the sorted list of unique labels
labels = sorted(list(set(model_y_test) | set(y_model_pred)))
#class_names = list(X_test["Threat Level"].unique())
#Dynamically determine the classes names: mapping class lebelsas level_mapping = {"Low": 0, "Medium": 1, "High": 2, "Critical": 3}
level_mapping = {0: "Low", 1: "Medium", 2: "High", 3: "Critical"}
class_names = [level_mapping.get(label) for label in labels]
#class_names = list(level_mapping.keys())
#class_names = labels
cm = confusion_matrix(model_y_test, y_model_pred, labels=labels)
# create cm data frame
confusion_matrix_df = pd.DataFrame(cm, index=class_names, columns=class_names)
#confusion_matrix_df = confusion_matrix_df_.rename(level_mapping, index=level_mapping)
print("\n" + model_name + " Confusion Matrix:\n")
#display(round(confusion_matrix_df,2))
# Create the heatmap
plt.figure(figsize=(4, 3))
heatmap = sns.heatmap(
round(confusion_matrix_df,2),
annot=True,
fmt='d',
cmap=custom_cmap,
xticklabels=class_names,
yticklabels=class_names
)
# Get the axes object
ax = heatmap.axes
# Set the x-axis label
ax.set_xlabel("Predicted Class")
# Move the x-axis label to the top
ax.xaxis.set_label_position('top')
ax.xaxis.tick_top()
#Set the y-axis label (title)
ax.set_ylabel("Actual Class")
# Set the overall plot title
plt.title("Confusion Matrix\n")
# Adjust subplot parameters to give more space at the top
plt.subplots_adjust(top=0.85)
# Display the plot
plt.show()
#print("\n" + model_name + " classification_report:\n")
#report = classification_report(model_y_test, y_model_pred, target_names=class_names, output_dict=True)
#display(pd.DataFrame(report).transpose())
print("\n" + model_name + " Agreggated Peformance Metrics:\n")
metrics_dic = get_metrics(model_y_test, y_model_pred, report)
metrics_df = pd.DataFrame(metrics_dic.items(), columns=['Metric', 'Value'])
display(metrics_df)
print("\nOverall Model Accuracy : ", metrics_dic.get("Overall Model Accuracy ", 0))
return metrics_dic
#----------------------------------------
def create_scatter_plot(data, x, y, hue, ax, x_label=None, y_label=None):
"""Generate scatter plot for anomalies vs normal points."""
sns.scatterplot(x=x, y=y, hue=hue, palette={0: 'blue', 1: 'red'}, data=data, ax=ax)
ax.set_title("Anomalies (Red) vs Normal Points (Blue)")
ax.set_xlabel(x_label or x)
ax.set_ylabel(y_label or y)
def create_roc_curve(data, anomaly_score, is_anomaly, ax):
"""Generate ROC curve and calculate AUC."""
fpr, tpr, _ = roc_curve(data[is_anomaly], data[anomaly_score])
roc_auc = auc(fpr, tpr)
ax.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')
ax.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
ax.set_xlabel('False Positive Rate')
ax.set_ylabel('True Positive Rate')
ax.set_title('Receiver Operating Characteristic (ROC) Curve')
ax.legend(loc="lower right")
def create_precision_recall_curve(data, anomaly_score, is_anomaly, ax):
"""Generate Precision-Recall Curve."""
precision, recall, _ = precision_recall_curve(data[is_anomaly], data[anomaly_score])
ax.plot(recall, precision, color='purple', lw=2)
ax.set_xlabel("Recall")
ax.set_ylabel("Precision")
ax.set_title("Precision-Recall Curve")
def visualizing_model_performance_pipeline(data, x, y, anomaly_score, is_anomaly, title=None):
"""Pipeline to visualize scatter plot, ROC curve, and Precision-Recall curve."""
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(18, 6))
fig.suptitle("Model Performance Visualization\n")
# Generate Scatter Plot
create_scatter_plot(data, x, y, hue=is_anomaly, ax=ax1, x_label=x, y_label=y)
# Generate ROC Curve
create_roc_curve(data, anomaly_score, is_anomaly, ax=ax2)
# Generate Precision-Recall Curve
create_precision_recall_curve(data, anomaly_score, is_anomaly, ax=ax3)
# Adjust layout and set title
plt.tight_layout()
if title:
plt.suptitle(title)
plt.show()
# ------------------------------------------ Supervised Learning Models ----------------------------
# Random Forest
def RandomForest_detect_anomalies(X_train, y_train, X_test, y_test):
rf_X_train = X_train.copy()
rf_y_train = y_train.copy()
rf_X_test = X_test.copy()
rf_y_test = y_test.copy()
# Define the Random Forest Classifier:
#creates a Random Forest classifier object with a fixed random
#state (random_state=42) for reproducibility.
rf = RandomForestClassifier(random_state=42)
#Defines the grid of hyperparameters to search through. Here, we are trying two values
#for n_estimators (number of trees) and three values for max_depth (maximum depth of trees).
#None for max_depth means the tree can grow indefinitely.
rf_params = {'n_estimators': [100, 200], 'max_depth': [10, 15, None]}
#Create GridSearchCV Object:cv=5: This specifies 5-fold cross-validation
#(it will split the training data into 5 folds and train the model on 4 folds
#while evaluating on the remaining fold, repeating this 5 times).
#scoring='accuracy': This tells GridSearchCV to use accuracy as the evaluation metric.
#Note: You can use other metrics like F1 score or precision-recall depending on your problem.
rf_grid = GridSearchCV(rf, rf_params, cv=5, scoring='accuracy')
# Train the model:This line trains the GridSearchCV object on the
#training data (X_train and y_train). It essentially trains a Random Forest model
#with each combination of hyperparameters in the grid on the training data using
#cross-validation and selects the one with the best accuracy.
rf_grid.fit(rf_X_train, rf_y_train)
#This retrieves the Random Forest model with the best hyperparameter combination
#based on the chosen scoring metric (accuracy in this case).
rf_best_model = rf_grid.best_estimator_
#This line uses the best model (rf_best) to make predictions on the test data (X_test).
y_rf_pred = rf_best_model.predict(rf_X_test)
rf_X_test["rf_anomaly_score"] = y_rf_pred
# Mark anomalies
rf_X_test ["rf_is_anomaly"] = rf_X_test["rf_anomaly_score"] == 1
print("\nRandom Forest\n")
#display(rf_X_test.head())
concat_copy_rf_X_y__test_y_pred = concatenate_model_data("rf", rf_X_test, rf_y_test, y_rf_pred)
display(concat_copy_rf_X_y__test_y_pred.head())
rf_metrics_dic = print_model_performance_report("Random Forest", rf_y_test, y_rf_pred)
# Model Performance Visualisation.
visualizing_model_performance_pipeline(
data=rf_X_test,
x="Session Duration in Second",
y= "Data Transfer MB",
anomaly_score="rf_anomaly_score",
is_anomaly="rf_is_anomaly",
title="Model Performance Visualization\n"
)
return rf_y_test, y_rf_pred, rf_best_model, rf_X_test, rf_metrics_dic
# Gradient Boosting
def GradientBoosting_detect_anomalies(X_train, y_train, X_test, y_test):
gb_X_train = X_train.copy()
gb_y_train = y_train.copy()
gb_X_test = X_test.copy()
gb_y_test = y_test.copy()
gb = GradientBoostingClassifier(random_state=42)
gb_params = {'n_estimators': [100, 200], 'learning_rate': [0.01, 0.1]}
gb_grid = GridSearchCV(gb, gb_params, cv=5, scoring='accuracy')
gb_grid.fit(gb_X_train, gb_y_train)
gb_best_model = gb_grid.best_estimator_
y_gb_pred = gb_best_model.predict(gb_X_test) # Probability of class 1 (anomaly)
gb_X_test["gb_anomaly_score"] = y_gb_pred
# Mark anomalies
gb_X_test ["gb_is_anomaly"] = gb_X_test["gb_anomaly_score"] == 1
print("\nGradient Boosting\n")
#display(gb_X_test.head())
concat_copy_gb_X_y__test_y_pred = concatenate_model_data("gb", gb_X_test, gb_y_test, y_gb_pred)
display(concat_copy_gb_X_y__test_y_pred.head())
gb_metrics_dic = print_model_performance_report("Gradient Boosting", gb_y_test, y_gb_pred)
# Model Performance Visualisation.
visualizing_model_performance_pipeline(
data=gb_X_test,
x="Session Duration in Second",
y= "Data Transfer MB",
anomaly_score="gb_anomaly_score",
is_anomaly="gb_is_anomaly",
title="Model Performance Visualization"
)
return gb_y_test, y_gb_pred, gb_best_model, gb_X_test, gb_metrics_dic
# -------------------------- Unsupervised Anomaly Detection Models --------------------------
# Isolation Forest
def isolation_forest_detect_anomalies(X_train, y_train, X_test, y_test):
iso_forest_X_train = X_train.copy()
iso_forest_y_train = y_train.copy()
iso_forest_X_test = X_test.copy()
iso_forest_y_test = y_test.copy()
#iso_forest_augmented_df = concatenate_data_along_columns(X_augmented, y_augmented)
iso_forest = IsolationForest(n_estimators=100, contamination=0.05, random_state=42)
iso_forest.fit(iso_forest_X_train)
y_iso_preds = iso_forest.predict(iso_forest_X_test)
scores = iso_forest.decision_function(iso_forest_X_test)
iso_preds = [1 if pred == -1 else 0 for pred in y_iso_preds] # -1 is anomaly in Isolation Forest
#iso_forest_X_test["iso_forest_anomaly_score"] = iso_preds
iso_forest_X_test["iso_forest_anomaly_score"] = scores
# Mark anomalies
iso_forest_X_test ["iso_forest_is_anomaly"] = iso_forest_X_test["iso_forest_anomaly_score"] == 1
print("\nIsolation Forest\n")
#display(iso_forest_X_test.head())
#concat_copy_iso_forest_X_y__test_y_pred = concatenate_model_data("iso", iso_forest_X_test, iso_forest_y_test, iso_preds)
concat_copy_iso_forest_X_y__test_y_pred = concatenate_model_data("iso", iso_forest_X_test, iso_forest_y_test, y_iso_preds)
display(concat_copy_iso_forest_X_y__test_y_pred.head())
iso_forest_metrics_dic = print_model_performance_report("Isolation Forest", iso_forest_y_test, iso_preds)
# Model Performance Visualisation.
visualizing_model_performance_pipeline(
data=iso_forest_X_test,
x="Session Duration in Second",
y= "Data Transfer MB",
anomaly_score="iso_forest_anomaly_score",
is_anomaly="iso_forest_is_anomaly",
title="Model Performance Visualization\n"
)
return iso_forest_y_test, iso_preds, iso_forest, iso_forest_X_test, iso_forest_metrics_dic
# Autoencoder for Anomaly Detection
def autoencoder_detect_anomalies(X_train, y_train, X_test, y_test):
autoencoder_X_train = X_train.copy()
autoencoder_y_train = y_train.copy()
autoencoder_X_test = X_test.copy()
autoencoder_y_test = y_test.copy()
#autoencoder_augmented_df = concatenate_data_along_columns(X_augmented, y_augmented)
def create_autoencoder(input_dim):
model = Sequential([
Dense(16, activation='relu', input_shape=(input_dim,)),
Dense(8, activation='relu'),
Dense(4, activation='relu'),
Dense(8, activation='relu'),
Dense(16, activation='relu'),
Dense(input_dim, activation='sigmoid')
])
model.compile(optimizer=Adam(learning_rate=0.001), loss='mse')
return model
autoencoder = create_autoencoder(autoencoder_X_train.shape[1])
early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)
history = autoencoder.fit(autoencoder_X_train, autoencoder_X_train, epochs=100,
batch_size=32, validation_split=0.1, callbacks=[early_stopping])
# Detect anomalies based on reconstruction error
reconstruction_error = np.mean(np.square(autoencoder_X_test - autoencoder.predict(autoencoder_X_test)), axis=1)
threshold = np.percentile(reconstruction_error, 95) # Set threshold for anomaly
y_autoencoder_preds = [1 if error > threshold else 0 for error in reconstruction_error]
autoencoder_X_test["autoencoder_anomaly_score"] = y_autoencoder_preds
autoencoder_X_test ["autoencoder_is_anomaly"] = autoencoder_X_test["autoencoder_anomaly_score"] == 1
print("\nAutoencoder\n")
#display(autoencoder_X_test.head())
concat_copy_autoencoder_X_y__test_y_pred = concatenate_model_data("autoencoder", autoencoder_X_test, autoencoder_y_test, y_autoencoder_preds)
display(concat_copy_autoencoder_X_y__test_y_pred.head())
autoencoder_metrics_dic = print_model_performance_report("Autoencoder", autoencoder_y_test, y_autoencoder_preds)
# Model Performance Visualisation.
visualizing_model_performance_pipeline(
data=autoencoder_X_test,
x="Session Duration in Second",
y= "Data Transfer MB",
anomaly_score="autoencoder_anomaly_score",
is_anomaly="autoencoder_is_anomaly",
title="Model Performance Visualization\n"
)
return autoencoder_y_test, y_autoencoder_preds, autoencoder, autoencoder_X_test, autoencoder_metrics_dic
# One-Class SVM
def OneClassSVM_detect_anomalies(X_train, y_train, X_test, y_test):
OneClassSVM_X_train = X_train.copy()
OneClassSVM_y_train = y_train.copy()
OneClassSVM_X_test = X_test.copy()
OneClassSVM_y_test = y_test.copy()
#augmented_OneClassSVM_df = concatenate_data_along_columns(X_augmented, y_augmented)
one_class_svm = OneClassSVM(kernel="rbf", gamma=0.001, nu=0.05) #gamma = 0.1
one_class_svm.fit(OneClassSVM_X_train)
y_svm_preds = one_class_svm.fit_predict(OneClassSVM_X_test)
y_svm_preds = [1 if pred == -1 else 0 for pred in y_svm_preds] # -1 is anomaly in Isolation Forest
OneClassSVM_X_test["one_class_svm_anomaly_score"] = y_svm_preds
# Mark anomalies
OneClassSVM_X_test ["one_class_svm_is_anomaly"] = OneClassSVM_X_test["one_class_svm_anomaly_score"] == 1
print("\nOneClassSVM\n")
#display(OneClassSVM_X_test.head())
concat_copy_OneClassSVM_X_y__test_y_pred = concatenate_model_data("OneClassSVM", OneClassSVM_X_test, OneClassSVM_y_test, y_svm_preds)
one_class_svm_metrics_dic = print_model_performance_report("one_class_svm", OneClassSVM_y_test, y_svm_preds)
# Model Performance Visualisation.
visualizing_model_performance_pipeline(
data=OneClassSVM_X_test,
x="Session Duration in Second",
y= "Data Transfer MB",
anomaly_score="one_class_svm_anomaly_score",
is_anomaly="one_class_svm_is_anomaly",
title="Model Performance Visualization\n"
)
return OneClassSVM_y_test, y_svm_preds, one_class_svm, OneClassSVM_X_test, one_class_svm_metrics_dic
# Local Outlier Factor
def Local_Outlier_Factor_detect_anomalies(X_train, y_train, X_test, y_test):
lof_X_train = X_train.copy()
lof_y_train = y_train.copy()
lof_X_test = X_test.copy()
lof_y_test = y_test.copy()
#augmented_Local_Outlier_Factor_df = concatenate_data_along_columns(X_augmented, y_augmented)
lof_model = LocalOutlierFactor(n_neighbors=20, contamination=0.1, novelty=True) # contamination=0.05
lof_model.fit(lof_X_train)
#y_lof_pred = lof_model.fit_predict(lof_X_test)
y_lof_pred = lof_model.predict(lof_X_test)
y_lof_pred = [1 if pred == -1 else 0 for pred in y_lof_pred] # -1 is anomaly in Isolation Forest
lof_X_test["Local_Outlier_Factor_anomaly_score"] = y_lof_pred
# Mark anomalies
lof_X_test ["Local_Outlier_Factor_is_anomaly"] = lof_X_test["Local_Outlier_Factor_anomaly_score"] == 1
display(lof_X_test.head())
print("\nLocal Outlier Factor\n")
#display(lof_X_test.head())
concat_copy_lof_X_y__test_y_pred = concatenate_model_data("lof", lof_X_test, lof_y_test, y_lof_pred)
display(concat_copy_lof_X_y__test_y_pred.head())
lof_metrics_dic = print_model_performance_report("Local Outlier Factor", lof_y_test, y_lof_pred)
# Model Performance Visualisation.
visualizing_model_performance_pipeline(
data=lof_X_test,
x="Session Duration in Second",
y= "Data Transfer MB",
anomaly_score="Local_Outlier_Factor_anomaly_score",
is_anomaly="Local_Outlier_Factor_is_anomaly",
title="Model Performance Visualization\n"
)
return lof_y_test, y_lof_pred, lof_model, lof_X_test, lof_metrics_dic
# Density-Based Spatial Clustering of Applications with Noise(DBSCAN)
def dbscan_detect_anomalies(X_train, y_train, X_test, y_test):
dbscan_X_train = X_train.copy()
dbscan_y_train = y_train.copy()
dbscan_X_test = X_test.copy()
dbscan_y_test = y_test.copy()
#augmented_dbscan_df = concatenate_data_along_columns(X_augmented, y_augmented)
dbscan = DBSCAN(eps=0.5, min_samples=5)
dbscan.fit(dbscan_X_train)
y_dbscan_pred = dbscan.fit_predict(dbscan_X_test)
#Convert y_true (ground-truth labels)and #Convert DBSCAN Labels to Binary 1 for anomalies, 0 for normal
y_dbscan_pred = np.where(y_dbscan_pred == -1, 1, 0)
dbscan_X_test["dbscan_anomaly_score"] = y_dbscan_pred
dbscan_X_test['is_anomaly_dbscan'] = dbscan_X_test['dbscan_anomaly_score'] == 1
print("\nDensity-Based Spatial Clustering of Applications with Noise(DBSCAN)\n")
#display(dbscan_X_test.head())
concat_copy_dbscan_X_y__test_y_pred = concatenate_model_data("dbscan", dbscan_X_test, dbscan_y_test, y_dbscan_pred)
display(concat_copy_dbscan_X_y__test_y_pred.head())
dbscan_metrics_dic = print_model_performance_report("DBSCAN", dbscan_y_test, y_dbscan_pred)
# Model Performance Visualisation.
visualizing_model_performance_pipeline(
data=dbscan_X_test,
x="Session Duration in Second",
y= "Data Transfer MB",
anomaly_score="dbscan_anomaly_score",
is_anomaly="is_anomaly_dbscan",
title="Model Performance Visualization\n"
)
return dbscan_y_test, y_dbscan_pred, dbscan, dbscan_X_test, dbscan_metrics_dic
# Long Short-Term Memory(LSTM) Model
def lstm_detect_anomalies(X_train, y_train, X_test, y_test ):
timesteps =1
n_features = X_train.shape[1]
threshold_percentile=95
copy_X_train = X_train.copy()
copy_y_train = y_train.copy()
copy_X_test = X_test.copy()
copy_y_test = y_test.copy()
def reshape_for_lstm(data, timesteps, n_features):
return data.reshape((data.shape[0], timesteps, n_features))
# Reshape data for LSTM
X_train_lstm = reshape_for_lstm(np.array(copy_X_train), timesteps, n_features)
X_test_lstm = reshape_for_lstm(np.array(copy_X_test), timesteps, n_features)
# Define LSTM model architecture
lstm_model = Sequential([
LSTM(64, input_shape=(timesteps, n_features), return_sequences=True),
Dropout(0.2),
LSTM(32, return_sequences=False),
Dropout(0.2),
Dense(n_features) # Output layer matches the feature count for reconstruction
])
# Compile and train the model
lstm_model.compile(optimizer='adam', loss='mse')
# Train the model
early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)
lstm_model.fit(X_train_lstm, X_train_lstm, epochs=50, batch_size=32, validation_split=0.1, callbacks=[early_stopping])
# Make predictions on test set
X_test_preds = lstm_model.predict(X_test_lstm)
# Calculate reconstruction error and MSE
reconstruction_error = np.mean(np.abs(X_test_lstm - X_test_preds), axis=1)
test_mse = np.mean(np.power(X_test_lstm - X_test_preds, 2), axis=(1, 2))
# Set anomaly threshold based on reconstruction error percentiles
threshold = np.percentile(test_mse, threshold_percentile)
copy_X_test["lstm_anomaly_score"] = test_mse
copy_X_test["lstm_is_anomaly"] = copy_X_test["lstm_anomaly_score"] > threshold
y_lstm_pred = copy_X_test["lstm_is_anomaly"].astype(int)
print("\Long Short-Term Memory(LSTM) Model\n")
#display(copy_X_test.head())
concat_copy_lstm_X_y__test_y_pred = concatenate_model_data("lstm", copy_X_test, copy_y_test, y_lstm_pred)
display(concat_copy_lstm_X_y__test_y_pred.head())
lstm_metrics_dic = print_model_performance_report("LSTM", y_test, y_lstm_pred)
# Model Performance Visualisation.
visualizing_model_performance_pipeline(
data=copy_X_test,
x="Session Duration in Second",
y= "Data Transfer MB",
anomaly_score="lstm_anomaly_score",
is_anomaly="lstm_is_anomaly",
title="Model Performance Visualization\n"
)
return copy_y_test, y_lstm_pred, lstm_model, test_mse, copy_X_test, lstm_metrics_dic
# K-means Clustering
def kmeans_clustering(X_train, y_train, X_test, y_test, n_clusters=2):
copy_X_train = X_train.copy()
copy_y_train = y_train.copy()
copy_X_test = X_test.copy()
copy_y_test = y_test.copy()
#augmented_kmean_df = concatenate_data_along_columns(X_augmented, y_augmented)
K_mean_model = KMeans(n_clusters=n_clusters, random_state=42)
K_mean_model.fit(copy_X_train)
y_kmeans_pred = K_mean_model.fit_predict(copy_X_test)
# Determine outliers by distance from cluster centroids
distances = np.linalg.norm(copy_X_test - K_mean_model.cluster_centers_[y_kmeans_pred], axis=1)
threshold = np.percentile(distances, 95)
preds = np.where(distances > threshold, 1, 0)
copy_X_test["kmeans_anomaly_score"] = preds
copy_X_test["is_anomaly_kmeans"] = copy_X_test["kmeans_anomaly_score"] == 1
print("\nK-Means\n")
#display(copy_X_test.head())
concat_copy_kmeans_X_y__test_y_pred = concatenate_model_data("kmeans", copy_X_test, copy_y_test, y_kmeans_pred)
display(concat_copy_kmeans_X_y__test_y_pred.head())
kmeans_metrics_dic = print_model_performance_report("k-means", copy_y_test, y_kmeans_pred)
# Model Performance Visualisation.
visualizing_model_performance_pipeline(
data=copy_X_test,
x="Session Duration in Second",
y= "Data Transfer MB",
anomaly_score="kmeans_anomaly_score",
is_anomaly="is_anomaly_kmeans",
title="Model Performance Visualization\n"
)
return copy_y_test, y_kmeans_pred, K_mean_model, copy_X_test, kmeans_metrics_dic
#------------------------------------------------------
#-----------------------------------------------------
#Models Training And Evaluation
def models_training_and_evaluation(X_train, y_train, X_test, y_test, X_augmented, y_augmented):
#Supervised Learning Models
y_rf_test, y_rf_pred, rf_best_model, rf_X_test_and_anomaly_df, rf_metrics_dic = RandomForest_detect_anomalies(X_train, y_train, X_test, y_test)
y_gb_test, y_gb_pred, gb_best_model, gb_X_test_and_anomaly_df, gb_metrics_dic = GradientBoosting_detect_anomalies(X_train, y_train, X_test, y_test)
#Unsupervised Anomaly Detection Models
y_iso_test, y_iso_preds, iso_forest_model, iso_forest_X_test_and_anomaly_df, iso_forest_metrics_dic = isolation_forest_detect_anomalies(X_train, y_train, X_test, y_test)
y_autoencoder_test, y_autoencoder_preds, autoencoder_model, autoencoder_X_test_and_anomaly_df, autoencoder_metrics_dic = autoencoder_detect_anomalies(X_train, y_train, X_test, y_test)
y_svm_test, y_svm_preds, one_class_svm_model, one_class_svm_X_test_and_anomaly_df, one_class_svm_metrics_dic = OneClassSVM_detect_anomalies(X_train, y_train, X_test, y_test)
y_lof_test, y_lof_pred, lof_model, lof_X_test_and_anomaly_df, lof_metrics_dic = Local_Outlier_Factor_detect_anomalies(X_train, y_train, X_test, y_test)
y_dbscan_test, y_dbscan_pred, dbscan_model, dbscan_X_test_and_anomaly_df, dbscan_metrics_dic = dbscan_detect_anomalies(X_train, y_train, X_test, y_test)
y_lstm_test, y_lstm_preds, lstm_model, mse, lstm_X_test_and_anomaly_df, lstm_metrics_dic = lstm_detect_anomalies(X_train, y_train, X_test, y_test)
y_kmeans_test, y_kmeans_pred, K_mean_model, kmeans_X_test_and_anomaly_df, kmeans_metrics_dic = kmeans_clustering(X_train, y_train, X_test, y_test, n_clusters=2)
models_dic = {"RandomForest" : rf_best_model,
"GradientBoosting" : gb_best_model ,
"IsolationForest" : iso_forest_model,
"Autoencoder" : autoencoder_model,
"OneClassSVM" : one_class_svm_model,
"LocalOutlierFactor" : lof_model,
"DBSCAN" : dbscan_model,
"LSTM" : lstm_model,
"KMeans" : K_mean_model}
model_metrics_results_dic = {"RandomForest" : rf_metrics_dic,
"GradientBoosting" : gb_metrics_dic,
"IsolationForest" : iso_forest_metrics_dic,
"Autoencoder" : autoencoder_metrics_dic,
"OneClassSVM" : one_class_svm_metrics_dic,
"LocalOutlierFactor" : lof_metrics_dic,
"DBSCAN" : dbscan_metrics_dic,
"LSTM" : lstm_metrics_dic,
"KMeans" : kmeans_metrics_dic}
return model_metrics_results_dic, models_dic
#-----------------Select Best Model based on Overall Model Accuracy
def select_best_model(results, models_dic):
best_model_name = max(results, key=lambda x: results[x].get("Overall Model Accuracy", 0))
best_model = models_dic[best_model_name]
best_model_metric = results[best_model_name].get("Overall Model Accuracy", 0)
print(f"\nBest performing model: {best_model}")
print(f"\nBest model metric: {best_model_metric}")
display(results[best_model_name])
return best_model_name, best_model
#---------------------------------------------------Winning Model Deployment------------------------------------------------
def deploy_best_model(model_deployment_path_folder, best_model_name, best_model):
model_path = f"{model_deployment_path_folder}/" + best_model_name +"_best_model.pkl"
joblib.dump(best_model, model_path)
print(f"Best model saved to: {model_path}")
return model_path
#model_path = deploy_best_model(best_model_name, best_model)
# ---------------------------------------Model Development Pipeline Function---------------------------------------------
def model_development_pipeline( ):
augmented_df = load_dataset("/content/drive/My Drive/Cybersecurity Data/x_y_augmented_data_google_drive.csv")
model_deployment_path_to_google_drive = "/content/drive/My Drive/Model deployment"
#fe_processed_df, loaded_label_encoders, num_fe_scaler = load_objects_from_drive()
X_augmented = augmented_df.drop(columns=["Threat Level"])
y_augmented = augmented_df["Threat Level"]
features_engineering_columns = X_augmented.columns.tolist()
X_train, X_test, y_train, y_test = deta_splitting(X_augmented, y_augmented, features_engineering_columns)
#Model training and evolution
model_metrics_results_dic, models_dic = models_training_and_evaluation( X_train, y_train, X_test, y_test, X_augmented, y_augmented)
#Select Best Model based on Overall Model Accuracy or other relevant metrics
best_model_name, best_model = select_best_model(model_metrics_results_dic, models_dic)
#--Winning Model Deeployment--------
model_path = deploy_best_model(model_deployment_path_to_google_drive, best_model_name, best_model)
#setting model_development_pipeline dic
model_development_pipeline_dic = {
"model_metrics_results_dic": model_metrics_results_dic,
"models_dic": models_dic,
"best_model_name": best_model_name,
"best_model": best_model,
"model_path": model_path
}
return model_development_pipeline_dic
#return model_metrics_results_dic, models_dic, best_model_name, best_model, model_path
if __name__ == "__main__":
model_development_pipeline_dic = model_development_pipeline()
Random Forest rfReport
| Issue Response Time Days | Impact Score | Cost | Session Duration in Second | Num Files Accessed | Login Attempts | Data Transfer MB | CPU Usage % | Memory Usage MB | Threat Score | rf_anomaly_score | rf_is_anomaly | Threat Level | rfy_pred | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1760 | 0.431462 | 0.087994 | 0.477184 | 0.398757 | 0.218439 | 0.256280 | 0.468704 | 0.653963 | 0.350027 | 0.031950 | 2 | False | 2 | 2 |
| 3016 | 0.872655 | 0.479052 | 0.104624 | 0.208113 | 0.707827 | 0.440162 | 0.778903 | 0.218079 | -0.505053 | 0.890502 | 0 | False | 0 | 0 |
| 1770 | 0.297337 | 0.087994 | 0.632004 | 0.271800 | 0.218439 | 0.237301 | 0.333192 | 0.681854 | 0.285789 | 0.022463 | 2 | False | 2 | 2 |
| 3703 | 0.447422 | 0.016336 | -0.361576 | -0.298874 | 0.473709 | 0.185849 | -0.291274 | 0.187664 | -0.198874 | 0.880715 | 0 | False | 0 | 0 |
| 2099 | 0.556140 | 0.223489 | 0.355953 | 0.424064 | 0.239386 | 0.436728 | 0.355275 | 0.500389 | 0.323306 | 0.162678 | 2 | False | 2 | 2 |
Random Forest classification_report:
precision recall f1-score support
0 0.97 1.00 0.99 470
1 0.93 0.80 0.86 35
2 0.99 0.97 0.98 266
3 0.82 0.79 0.81 29
accuracy 0.97 800
macro avg 0.93 0.89 0.91 800
weighted avg 0.97 0.97 0.97 800
| precision | recall | f1-score | support | |
|---|---|---|---|---|
| 0 | 0.975000 | 0.995745 | 0.985263 | 470.0000 |
| 1 | 0.933333 | 0.800000 | 0.861538 | 35.0000 |
| 2 | 0.988550 | 0.973684 | 0.981061 | 266.0000 |
| 3 | 0.821429 | 0.793103 | 0.807018 | 29.0000 |
| accuracy | 0.972500 | 0.972500 | 0.972500 | 0.9725 |
| macro avg | 0.929578 | 0.890633 | 0.908720 | 800.0000 |
| weighted avg | 0.972115 | 0.972500 | 0.971991 | 800.0000 |
Random Forest Confusion Matrix:
Random Forest Agreggated Peformance Metrics:
| Metric | Value | |
|---|---|---|
| 0 | Precision (Macro) | 0.929578 |
| 1 | Recall (Macro) | 0.890633 |
| 2 | F1 Score (Macro) | 0.908720 |
| 3 | Precision (Weighted) | 0.972115 |
| 4 | Recall (Weighted) | 0.972500 |
| 5 | F1 Score (Weighted) | 0.971991 |
| 6 | Accuracy | 0.972500 |
| 7 | Overall Model Accuracy | 0.972500 |
Overall Model Accuracy : 0.9725
Gradient Boosting gbReport
| Issue Response Time Days | Impact Score | Cost | Session Duration in Second | Num Files Accessed | Login Attempts | Data Transfer MB | CPU Usage % | Memory Usage MB | Threat Score | gb_anomaly_score | gb_is_anomaly | Threat Level | gby_pred | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1760 | 0.431462 | 0.087994 | 0.477184 | 0.398757 | 0.218439 | 0.256280 | 0.468704 | 0.653963 | 0.350027 | 0.031950 | 2 | False | 2 | 2 |
| 3016 | 0.872655 | 0.479052 | 0.104624 | 0.208113 | 0.707827 | 0.440162 | 0.778903 | 0.218079 | -0.505053 | 0.890502 | 0 | False | 0 | 0 |
| 1770 | 0.297337 | 0.087994 | 0.632004 | 0.271800 | 0.218439 | 0.237301 | 0.333192 | 0.681854 | 0.285789 | 0.022463 | 2 | False | 2 | 2 |
| 3703 | 0.447422 | 0.016336 | -0.361576 | -0.298874 | 0.473709 | 0.185849 | -0.291274 | 0.187664 | -0.198874 | 0.880715 | 0 | False | 0 | 0 |
| 2099 | 0.556140 | 0.223489 | 0.355953 | 0.424064 | 0.239386 | 0.436728 | 0.355275 | 0.500389 | 0.323306 | 0.162678 | 2 | False | 2 | 2 |
Gradient Boosting classification_report:
precision recall f1-score support
0 0.99 1.00 0.99 470
1 0.97 0.83 0.89 35
2 0.98 0.99 0.99 266
3 0.92 0.83 0.87 29
accuracy 0.98 800
macro avg 0.96 0.91 0.94 800
weighted avg 0.98 0.98 0.98 800
| precision | recall | f1-score | support | |
|---|---|---|---|---|
| 0 | 0.985263 | 0.995745 | 0.990476 | 470.00000 |
| 1 | 0.966667 | 0.828571 | 0.892308 | 35.00000 |
| 2 | 0.981413 | 0.992481 | 0.986916 | 266.00000 |
| 3 | 0.923077 | 0.827586 | 0.872727 | 29.00000 |
| accuracy | 0.981250 | 0.981250 | 0.981250 | 0.98125 |
| macro avg | 0.964105 | 0.911096 | 0.935607 | 800.00000 |
| weighted avg | 0.980915 | 0.981250 | 0.980729 | 800.00000 |
Gradient Boosting Confusion Matrix:
Gradient Boosting Agreggated Peformance Metrics:
| Metric | Value | |
|---|---|---|
| 0 | Precision (Macro) | 0.964105 |
| 1 | Recall (Macro) | 0.911096 |
| 2 | F1 Score (Macro) | 0.935607 |
| 3 | Precision (Weighted) | 0.980915 |
| 4 | Recall (Weighted) | 0.981250 |
| 5 | F1 Score (Weighted) | 0.980729 |
| 6 | Accuracy | 0.981250 |
| 7 | Overall Model Accuracy | 0.981250 |
Overall Model Accuracy : 0.98125
Isolation Forest isoReport
| Issue Response Time Days | Impact Score | Cost | Session Duration in Second | Num Files Accessed | Login Attempts | Data Transfer MB | CPU Usage % | Memory Usage MB | Threat Score | iso_forest_anomaly_score | iso_forest_is_anomaly | Threat Level | isoy_pred | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1760 | 0.431462 | 0.087994 | 0.477184 | 0.398757 | 0.218439 | 0.256280 | 0.468704 | 0.653963 | 0.350027 | 0.031950 | 0.175727 | False | 2 | 1 |
| 3016 | 0.872655 | 0.479052 | 0.104624 | 0.208113 | 0.707827 | 0.440162 | 0.778903 | 0.218079 | -0.505053 | 0.890502 | -0.016353 | False | 0 | -1 |
| 1770 | 0.297337 | 0.087994 | 0.632004 | 0.271800 | 0.218439 | 0.237301 | 0.333192 | 0.681854 | 0.285789 | 0.022463 | 0.193939 | False | 2 | 1 |
| 3703 | 0.447422 | 0.016336 | -0.361576 | -0.298874 | 0.473709 | 0.185849 | -0.291274 | 0.187664 | -0.198874 | 0.880715 | 0.041515 | False | 0 | 1 |
| 2099 | 0.556140 | 0.223489 | 0.355953 | 0.424064 | 0.239386 | 0.436728 | 0.355275 | 0.500389 | 0.323306 | 0.162678 | 0.183357 | False | 2 | 1 |
Isolation Forest classification_report:
precision recall f1-score support
0 0.57 0.93 0.71 470
1 0.00 0.00 0.00 35
2 0.00 0.00 0.00 266
3 0.00 0.00 0.00 29
accuracy 0.55 800
macro avg 0.14 0.23 0.18 800
weighted avg 0.34 0.55 0.42 800
| precision | recall | f1-score | support | |
|---|---|---|---|---|
| 0 | 0.570871 | 0.934043 | 0.708636 | 470.00000 |
| 1 | 0.000000 | 0.000000 | 0.000000 | 35.00000 |
| 2 | 0.000000 | 0.000000 | 0.000000 | 266.00000 |
| 3 | 0.000000 | 0.000000 | 0.000000 | 29.00000 |
| accuracy | 0.548750 | 0.548750 | 0.548750 | 0.54875 |
| macro avg | 0.142718 | 0.233511 | 0.177159 | 800.00000 |
| weighted avg | 0.335387 | 0.548750 | 0.416324 | 800.00000 |
Isolation Forest Confusion Matrix:
Isolation Forest Agreggated Peformance Metrics:
| Metric | Value | |
|---|---|---|
| 0 | Precision (Macro) | 0.142718 |
| 1 | Recall (Macro) | 0.233511 |
| 2 | F1 Score (Macro) | 0.177159 |
| 3 | Precision (Weighted) | 0.335387 |
| 4 | Recall (Weighted) | 0.548750 |
| 5 | F1 Score (Weighted) | 0.416324 |
| 6 | Accuracy | 0.548750 |
| 7 | Overall Model Accuracy | 0.548750 |
Overall Model Accuracy : 0.54875
Epoch 1/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - loss: 0.1290 - val_loss: 0.0867 Epoch 2/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0720 - val_loss: 0.0608 Epoch 3/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0576 - val_loss: 0.0541 Epoch 4/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0487 - val_loss: 0.0489 Epoch 5/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0465 - val_loss: 0.0468 Epoch 6/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 0.0468 - val_loss: 0.0456 Epoch 7/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0439 - val_loss: 0.0446 Epoch 8/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 0.0416 - val_loss: 0.0437 Epoch 9/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 5ms/step - loss: 0.0425 - val_loss: 0.0429 Epoch 10/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 0.0420 - val_loss: 0.0417 Epoch 11/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 0.0399 - val_loss: 0.0406 Epoch 12/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 0.0388 - val_loss: 0.0400 Epoch 13/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 0.0387 - val_loss: 0.0396 Epoch 14/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 0.0393 - val_loss: 0.0393 Epoch 15/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 0.0370 - val_loss: 0.0390 Epoch 16/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0363 - val_loss: 0.0380 Epoch 17/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 0.0364 - val_loss: 0.0375 Epoch 18/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0343 - val_loss: 0.0373 Epoch 19/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0346 - val_loss: 0.0370 Epoch 20/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 0.0363 - val_loss: 0.0370 Epoch 21/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 0.0352 - val_loss: 0.0368 Epoch 22/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0375 - val_loss: 0.0367 Epoch 23/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0346 - val_loss: 0.0366 Epoch 24/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0350 - val_loss: 0.0367 Epoch 25/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - loss: 0.0343 - val_loss: 0.0364 Epoch 26/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - loss: 0.0346 - val_loss: 0.0363 Epoch 27/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0355 - val_loss: 0.0363 Epoch 28/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0351 - val_loss: 0.0361 Epoch 29/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0348 - val_loss: 0.0360 Epoch 30/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0351 - val_loss: 0.0357 Epoch 31/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0339 - val_loss: 0.0355 Epoch 32/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 0.0345 - val_loss: 0.0353 Epoch 33/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 5ms/step - loss: 0.0328 - val_loss: 0.0349 Epoch 34/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 0.0322 - val_loss: 0.0348 Epoch 35/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 0.0331 - val_loss: 0.0347 Epoch 36/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 0.0353 - val_loss: 0.0344 Epoch 37/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 0.0335 - val_loss: 0.0344 Epoch 38/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 0.0331 - val_loss: 0.0343 Epoch 39/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 0.0325 - val_loss: 0.0342 Epoch 40/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0307 - val_loss: 0.0341 Epoch 41/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0328 - val_loss: 0.0340 Epoch 42/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0323 - val_loss: 0.0340 Epoch 43/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - loss: 0.0324 - val_loss: 0.0339 Epoch 44/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0328 - val_loss: 0.0339 Epoch 45/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 0.0315 - val_loss: 0.0338 Epoch 46/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - loss: 0.0312 - val_loss: 0.0337 Epoch 47/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 0.0326 - val_loss: 0.0337 Epoch 48/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 0.0334 - val_loss: 0.0338 Epoch 49/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0311 - val_loss: 0.0337 Epoch 50/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0321 - val_loss: 0.0338 Epoch 51/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0316 - val_loss: 0.0337 Epoch 52/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0318 - val_loss: 0.0335 Epoch 53/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0316 - val_loss: 0.0336 Epoch 54/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 0.0301 - val_loss: 0.0335 Epoch 55/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - loss: 0.0308 - val_loss: 0.0335 Epoch 56/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 0.0310 - val_loss: 0.0335 Epoch 57/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0317 - val_loss: 0.0335 Epoch 58/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 5ms/step - loss: 0.0324 - val_loss: 0.0334 Epoch 59/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 5ms/step - loss: 0.0313 - val_loss: 0.0334 Epoch 60/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 0.0326 - val_loss: 0.0334 Epoch 61/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 0.0341 - val_loss: 0.0334 Epoch 62/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 0.0316 - val_loss: 0.0334 Epoch 63/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 0.0316 - val_loss: 0.0333 Epoch 64/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 0.0316 - val_loss: 0.0335 Epoch 65/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.0320 - val_loss: 0.0334 Epoch 66/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 0.0310 - val_loss: 0.0333 Epoch 67/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0308 - val_loss: 0.0333 Epoch 68/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0315 - val_loss: 0.0333 Epoch 69/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 0.0301 - val_loss: 0.0332 Epoch 70/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0305 - val_loss: 0.0333 Epoch 71/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0329 - val_loss: 0.0332 Epoch 72/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0307 - val_loss: 0.0332 Epoch 73/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0321 - val_loss: 0.0333 Epoch 74/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 0.0318 - val_loss: 0.0333 Epoch 75/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0312 - val_loss: 0.0332 Epoch 76/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0314 - val_loss: 0.0332 Epoch 77/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0312 - val_loss: 0.0331 Epoch 78/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 0.0324 - val_loss: 0.0330 Epoch 79/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0299 - val_loss: 0.0331 Epoch 80/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0308 - val_loss: 0.0330 Epoch 81/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0324 - val_loss: 0.0329 Epoch 82/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 0.0297 - val_loss: 0.0329 Epoch 83/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0307 - val_loss: 0.0327 Epoch 84/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 0.0314 - val_loss: 0.0326 Epoch 85/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 5ms/step - loss: 0.0296 - val_loss: 0.0325 Epoch 86/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 0.0297 - val_loss: 0.0325 Epoch 87/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 0.0300 - val_loss: 0.0324 Epoch 88/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 0.0307 - val_loss: 0.0324 Epoch 89/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 0.0307 - val_loss: 0.0323 Epoch 90/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 0.0307 - val_loss: 0.0323 Epoch 91/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 0.0299 - val_loss: 0.0324 Epoch 92/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 0.0298 - val_loss: 0.0322 Epoch 93/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 0.0302 - val_loss: 0.0323 Epoch 94/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0320 - val_loss: 0.0323 Epoch 95/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - loss: 0.0291 - val_loss: 0.0324 Epoch 96/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0297 - val_loss: 0.0322 Epoch 97/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 0.0288 - val_loss: 0.0322 Epoch 98/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0301 - val_loss: 0.0322 Epoch 99/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 5ms/step - loss: 0.0286 - val_loss: 0.0322 Epoch 100/100 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.0306 - val_loss: 0.0324 25/25 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step Autoencoder autoencoderReport
| Issue Response Time Days | Impact Score | Cost | Session Duration in Second | Num Files Accessed | Login Attempts | Data Transfer MB | CPU Usage % | Memory Usage MB | Threat Score | autoencoder_anomaly_score | autoencoder_is_anomaly | Threat Level | autoencodery_pred | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1760 | 0.431462 | 0.087994 | 0.477184 | 0.398757 | 0.218439 | 0.256280 | 0.468704 | 0.653963 | 0.350027 | 0.031950 | 0 | False | 2 | 0 |
| 3016 | 0.872655 | 0.479052 | 0.104624 | 0.208113 | 0.707827 | 0.440162 | 0.778903 | 0.218079 | -0.505053 | 0.890502 | 0 | False | 0 | 0 |
| 1770 | 0.297337 | 0.087994 | 0.632004 | 0.271800 | 0.218439 | 0.237301 | 0.333192 | 0.681854 | 0.285789 | 0.022463 | 0 | False | 2 | 0 |
| 3703 | 0.447422 | 0.016336 | -0.361576 | -0.298874 | 0.473709 | 0.185849 | -0.291274 | 0.187664 | -0.198874 | 0.880715 | 0 | False | 0 | 0 |
| 2099 | 0.556140 | 0.223489 | 0.355953 | 0.424064 | 0.239386 | 0.436728 | 0.355275 | 0.500389 | 0.323306 | 0.162678 | 0 | False | 2 | 0 |
Autoencoder classification_report:
precision recall f1-score support
0 0.57 0.91 0.70 470
1 0.00 0.00 0.00 35
2 0.00 0.00 0.00 266
3 0.00 0.00 0.00 29
accuracy 0.54 800
macro avg 0.14 0.23 0.17 800
weighted avg 0.33 0.54 0.41 800
| precision | recall | f1-score | support | |
|---|---|---|---|---|
| 0 | 0.565789 | 0.914894 | 0.699187 | 470.0000 |
| 1 | 0.000000 | 0.000000 | 0.000000 | 35.0000 |
| 2 | 0.000000 | 0.000000 | 0.000000 | 266.0000 |
| 3 | 0.000000 | 0.000000 | 0.000000 | 29.0000 |
| accuracy | 0.537500 | 0.537500 | 0.537500 | 0.5375 |
| macro avg | 0.141447 | 0.228723 | 0.174797 | 800.0000 |
| weighted avg | 0.332401 | 0.537500 | 0.410772 | 800.0000 |
Autoencoder Confusion Matrix:
Autoencoder Agreggated Peformance Metrics:
| Metric | Value | |
|---|---|---|
| 0 | Precision (Macro) | 0.141447 |
| 1 | Recall (Macro) | 0.228723 |
| 2 | F1 Score (Macro) | 0.174797 |
| 3 | Precision (Weighted) | 0.332401 |
| 4 | Recall (Weighted) | 0.537500 |
| 5 | F1 Score (Weighted) | 0.410772 |
| 6 | Accuracy | 0.537500 |
| 7 | Overall Model Accuracy | 0.537500 |
Overall Model Accuracy : 0.5375
OneClassSVM
OneClassSVMReport
one_class_svm classification_report:
precision recall f1-score support
0 0.57 0.92 0.70 470
1 0.02 0.03 0.03 35
2 0.00 0.00 0.00 266
3 0.00 0.00 0.00 29
accuracy 0.54 800
macro avg 0.15 0.24 0.18 800
weighted avg 0.34 0.54 0.41 800
| precision | recall | f1-score | support | |
|---|---|---|---|---|
| 0 | 0.568602 | 0.917021 | 0.701954 | 470.00 |
| 1 | 0.023810 | 0.028571 | 0.025974 | 35.00 |
| 2 | 0.000000 | 0.000000 | 0.000000 | 266.00 |
| 3 | 0.000000 | 0.000000 | 0.000000 | 29.00 |
| accuracy | 0.540000 | 0.540000 | 0.540000 | 0.54 |
| macro avg | 0.148103 | 0.236398 | 0.181982 | 800.00 |
| weighted avg | 0.335095 | 0.540000 | 0.413535 | 800.00 |
one_class_svm Confusion Matrix:
one_class_svm Agreggated Peformance Metrics:
| Metric | Value | |
|---|---|---|
| 0 | Precision (Macro) | 0.148103 |
| 1 | Recall (Macro) | 0.236398 |
| 2 | F1 Score (Macro) | 0.181982 |
| 3 | Precision (Weighted) | 0.335095 |
| 4 | Recall (Weighted) | 0.540000 |
| 5 | F1 Score (Weighted) | 0.413535 |
| 6 | Accuracy | 0.540000 |
| 7 | Overall Model Accuracy | 0.540000 |
Overall Model Accuracy : 0.54
| Issue Response Time Days | Impact Score | Cost | Session Duration in Second | Num Files Accessed | Login Attempts | Data Transfer MB | CPU Usage % | Memory Usage MB | Threat Score | Local_Outlier_Factor_anomaly_score | Local_Outlier_Factor_is_anomaly | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1760 | 0.431462 | 0.087994 | 0.477184 | 0.398757 | 0.218439 | 0.256280 | 0.468704 | 0.653963 | 0.350027 | 0.031950 | 0 | False |
| 3016 | 0.872655 | 0.479052 | 0.104624 | 0.208113 | 0.707827 | 0.440162 | 0.778903 | 0.218079 | -0.505053 | 0.890502 | 0 | False |
| 1770 | 0.297337 | 0.087994 | 0.632004 | 0.271800 | 0.218439 | 0.237301 | 0.333192 | 0.681854 | 0.285789 | 0.022463 | 0 | False |
| 3703 | 0.447422 | 0.016336 | -0.361576 | -0.298874 | 0.473709 | 0.185849 | -0.291274 | 0.187664 | -0.198874 | 0.880715 | 0 | False |
| 2099 | 0.556140 | 0.223489 | 0.355953 | 0.424064 | 0.239386 | 0.436728 | 0.355275 | 0.500389 | 0.323306 | 0.162678 | 0 | False |
Local Outlier Factor lofReport
| Issue Response Time Days | Impact Score | Cost | Session Duration in Second | Num Files Accessed | Login Attempts | Data Transfer MB | CPU Usage % | Memory Usage MB | Threat Score | Local_Outlier_Factor_anomaly_score | Local_Outlier_Factor_is_anomaly | Threat Level | lofy_pred | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1760 | 0.431462 | 0.087994 | 0.477184 | 0.398757 | 0.218439 | 0.256280 | 0.468704 | 0.653963 | 0.350027 | 0.031950 | 0 | False | 2 | 0 |
| 3016 | 0.872655 | 0.479052 | 0.104624 | 0.208113 | 0.707827 | 0.440162 | 0.778903 | 0.218079 | -0.505053 | 0.890502 | 0 | False | 0 | 0 |
| 1770 | 0.297337 | 0.087994 | 0.632004 | 0.271800 | 0.218439 | 0.237301 | 0.333192 | 0.681854 | 0.285789 | 0.022463 | 0 | False | 2 | 0 |
| 3703 | 0.447422 | 0.016336 | -0.361576 | -0.298874 | 0.473709 | 0.185849 | -0.291274 | 0.187664 | -0.198874 | 0.880715 | 0 | False | 0 | 0 |
| 2099 | 0.556140 | 0.223489 | 0.355953 | 0.424064 | 0.239386 | 0.436728 | 0.355275 | 0.500389 | 0.323306 | 0.162678 | 0 | False | 2 | 0 |
Local Outlier Factor classification_report:
precision recall f1-score support
0 0.59 0.90 0.72 470
1 0.14 0.34 0.20 35
2 0.00 0.00 0.00 266
3 0.00 0.00 0.00 29
accuracy 0.55 800
macro avg 0.18 0.31 0.23 800
weighted avg 0.36 0.55 0.43 800
| precision | recall | f1-score | support | |
|---|---|---|---|---|
| 0 | 0.594406 | 0.904255 | 0.717300 | 470.00000 |
| 1 | 0.141176 | 0.342857 | 0.200000 | 35.00000 |
| 2 | 0.000000 | 0.000000 | 0.000000 | 266.00000 |
| 3 | 0.000000 | 0.000000 | 0.000000 | 29.00000 |
| accuracy | 0.546250 | 0.546250 | 0.546250 | 0.54625 |
| macro avg | 0.183896 | 0.311778 | 0.229325 | 800.00000 |
| weighted avg | 0.355390 | 0.546250 | 0.430164 | 800.00000 |
Local Outlier Factor Confusion Matrix:
Local Outlier Factor Agreggated Peformance Metrics:
| Metric | Value | |
|---|---|---|
| 0 | Precision (Macro) | 0.183896 |
| 1 | Recall (Macro) | 0.311778 |
| 2 | F1 Score (Macro) | 0.229325 |
| 3 | Precision (Weighted) | 0.355390 |
| 4 | Recall (Weighted) | 0.546250 |
| 5 | F1 Score (Weighted) | 0.430164 |
| 6 | Accuracy | 0.546250 |
| 7 | Overall Model Accuracy | 0.546250 |
Overall Model Accuracy : 0.54625
Density-Based Spatial Clustering of Applications with Noise(DBSCAN) dbscanReport
| Issue Response Time Days | Impact Score | Cost | Session Duration in Second | Num Files Accessed | Login Attempts | Data Transfer MB | CPU Usage % | Memory Usage MB | Threat Score | dbscan_anomaly_score | is_anomaly_dbscan | Threat Level | dbscany_pred | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1760 | 0.431462 | 0.087994 | 0.477184 | 0.398757 | 0.218439 | 0.256280 | 0.468704 | 0.653963 | 0.350027 | 0.031950 | 0 | False | 2 | 0 |
| 3016 | 0.872655 | 0.479052 | 0.104624 | 0.208113 | 0.707827 | 0.440162 | 0.778903 | 0.218079 | -0.505053 | 0.890502 | 1 | True | 0 | 1 |
| 1770 | 0.297337 | 0.087994 | 0.632004 | 0.271800 | 0.218439 | 0.237301 | 0.333192 | 0.681854 | 0.285789 | 0.022463 | 0 | False | 2 | 0 |
| 3703 | 0.447422 | 0.016336 | -0.361576 | -0.298874 | 0.473709 | 0.185849 | -0.291274 | 0.187664 | -0.198874 | 0.880715 | 1 | True | 0 | 1 |
| 2099 | 0.556140 | 0.223489 | 0.355953 | 0.424064 | 0.239386 | 0.436728 | 0.355275 | 0.500389 | 0.323306 | 0.162678 | 0 | False | 2 | 0 |
DBSCAN classification_report:
precision recall f1-score support
0 0.45 0.57 0.50 470
1 0.00 0.03 0.01 35
2 0.00 0.00 0.00 266
3 0.00 0.00 0.00 29
accuracy 0.34 800
macro avg 0.11 0.15 0.13 800
weighted avg 0.26 0.34 0.29 800
| precision | recall | f1-score | support | |
|---|---|---|---|---|
| 0 | 0.447987 | 0.568085 | 0.500938 | 470.000 |
| 1 | 0.004902 | 0.028571 | 0.008368 | 35.000 |
| 2 | 0.000000 | 0.000000 | 0.000000 | 266.000 |
| 3 | 0.000000 | 0.000000 | 0.000000 | 29.000 |
| accuracy | 0.335000 | 0.335000 | 0.335000 | 0.335 |
| macro avg | 0.113222 | 0.149164 | 0.127327 | 800.000 |
| weighted avg | 0.263407 | 0.335000 | 0.294667 | 800.000 |
DBSCAN Confusion Matrix:
DBSCAN Agreggated Peformance Metrics:
| Metric | Value | |
|---|---|---|
| 0 | Precision (Macro) | 0.113222 |
| 1 | Recall (Macro) | 0.149164 |
| 2 | F1 Score (Macro) | 0.127327 |
| 3 | Precision (Weighted) | 0.263407 |
| 4 | Recall (Weighted) | 0.335000 |
| 5 | F1 Score (Weighted) | 0.294667 |
| 6 | Accuracy | 0.335000 |
| 7 | Overall Model Accuracy | 0.335000 |
Overall Model Accuracy : 0.335
Epoch 1/50 90/90 ━━━━━━━━━━━━━━━━━━━━ 6s 12ms/step - loss: 0.1469 - val_loss: 0.0945 Epoch 2/50 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: 0.0942 - val_loss: 0.0924 Epoch 3/50 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - loss: 0.0931 - val_loss: 0.0920 Epoch 4/50 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - loss: 0.0909 - val_loss: 0.0920 Epoch 5/50 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - loss: 0.0889 - val_loss: 0.0918 Epoch 6/50 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - loss: 0.0894 - val_loss: 0.0920 Epoch 7/50 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - loss: 0.0904 - val_loss: 0.0917 Epoch 8/50 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - loss: 0.0881 - val_loss: 0.0916 Epoch 9/50 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: 0.0890 - val_loss: 0.0917 Epoch 10/50 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - loss: 0.0881 - val_loss: 0.0920 Epoch 11/50 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - loss: 0.0903 - val_loss: 0.0918 Epoch 12/50 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - loss: 0.0898 - val_loss: 0.0916 Epoch 13/50 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - loss: 0.0890 - val_loss: 0.0916 Epoch 14/50 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - loss: 0.0894 - val_loss: 0.0919 Epoch 15/50 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - loss: 0.0891 - val_loss: 0.0918 Epoch 16/50 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - loss: 0.0890 - val_loss: 0.0917 Epoch 17/50 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - loss: 0.0922 - val_loss: 0.0920 Epoch 18/50 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - loss: 0.0918 - val_loss: 0.0916 Epoch 19/50 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - loss: 0.0884 - val_loss: 0.0918 Epoch 20/50 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - loss: 0.0919 - val_loss: 0.0919 Epoch 21/50 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - loss: 0.0897 - val_loss: 0.0919 Epoch 22/50 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - loss: 0.0903 - val_loss: 0.0917 Epoch 23/50 90/90 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - loss: 0.0894 - val_loss: 0.0919 25/25 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step \Long Short-Term Memory(LSTM) Model lstmReport
| Issue Response Time Days | Impact Score | Cost | Session Duration in Second | Num Files Accessed | Login Attempts | Data Transfer MB | CPU Usage % | Memory Usage MB | Threat Score | lstm_anomaly_score | lstm_is_anomaly | Threat Level | lstmy_pred | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1760 | 0.431462 | 0.087994 | 0.477184 | 0.398757 | 0.218439 | 0.256280 | 0.468704 | 0.653963 | 0.350027 | 0.031950 | 0.032707 | False | 2 | 0 |
| 3016 | 0.872655 | 0.479052 | 0.104624 | 0.208113 | 0.707827 | 0.440162 | 0.778903 | 0.218079 | -0.505053 | 0.890502 | 0.178171 | False | 0 | 0 |
| 1770 | 0.297337 | 0.087994 | 0.632004 | 0.271800 | 0.218439 | 0.237301 | 0.333192 | 0.681854 | 0.285789 | 0.022463 | 0.034010 | False | 2 | 0 |
| 3703 | 0.447422 | 0.016336 | -0.361576 | -0.298874 | 0.473709 | 0.185849 | -0.291274 | 0.187664 | -0.198874 | 0.880715 | 0.171802 | False | 0 | 0 |
| 2099 | 0.556140 | 0.223489 | 0.355953 | 0.424064 | 0.239386 | 0.436728 | 0.355275 | 0.500389 | 0.323306 | 0.162678 | 0.018449 | False | 2 | 0 |
LSTM classification_report:
precision recall f1-score support
0 0.57 0.91 0.70 470
1 0.00 0.00 0.00 35
2 0.00 0.00 0.00 266
3 0.00 0.00 0.00 29
accuracy 0.54 800
macro avg 0.14 0.23 0.17 800
weighted avg 0.33 0.54 0.41 800
| precision | recall | f1-score | support | |
|---|---|---|---|---|
| 0 | 0.565789 | 0.914894 | 0.699187 | 470.0000 |
| 1 | 0.000000 | 0.000000 | 0.000000 | 35.0000 |
| 2 | 0.000000 | 0.000000 | 0.000000 | 266.0000 |
| 3 | 0.000000 | 0.000000 | 0.000000 | 29.0000 |
| accuracy | 0.537500 | 0.537500 | 0.537500 | 0.5375 |
| macro avg | 0.141447 | 0.228723 | 0.174797 | 800.0000 |
| weighted avg | 0.332401 | 0.537500 | 0.410772 | 800.0000 |
LSTM Confusion Matrix:
LSTM Agreggated Peformance Metrics:
| Metric | Value | |
|---|---|---|
| 0 | Precision (Macro) | 0.141447 |
| 1 | Recall (Macro) | 0.228723 |
| 2 | F1 Score (Macro) | 0.174797 |
| 3 | Precision (Weighted) | 0.332401 |
| 4 | Recall (Weighted) | 0.537500 |
| 5 | F1 Score (Weighted) | 0.410772 |
| 6 | Accuracy | 0.537500 |
| 7 | Overall Model Accuracy | 0.537500 |
Overall Model Accuracy : 0.5375
K-Means kmeansReport
| Issue Response Time Days | Impact Score | Cost | Session Duration in Second | Num Files Accessed | Login Attempts | Data Transfer MB | CPU Usage % | Memory Usage MB | Threat Score | kmeans_anomaly_score | is_anomaly_kmeans | Threat Level | kmeansy_pred | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1760 | 0.431462 | 0.087994 | 0.477184 | 0.398757 | 0.218439 | 0.256280 | 0.468704 | 0.653963 | 0.350027 | 0.031950 | 0 | False | 2 | 1 |
| 3016 | 0.872655 | 0.479052 | 0.104624 | 0.208113 | 0.707827 | 0.440162 | 0.778903 | 0.218079 | -0.505053 | 0.890502 | 1 | True | 0 | 0 |
| 1770 | 0.297337 | 0.087994 | 0.632004 | 0.271800 | 0.218439 | 0.237301 | 0.333192 | 0.681854 | 0.285789 | 0.022463 | 0 | False | 2 | 1 |
| 3703 | 0.447422 | 0.016336 | -0.361576 | -0.298874 | 0.473709 | 0.185849 | -0.291274 | 0.187664 | -0.198874 | 0.880715 | 0 | False | 0 | 0 |
| 2099 | 0.556140 | 0.223489 | 0.355953 | 0.424064 | 0.239386 | 0.436728 | 0.355275 | 0.500389 | 0.323306 | 0.162678 | 0 | False | 2 | 1 |
k-means classification_report:
precision recall f1-score support
0 1.00 0.40 0.57 470
1 0.06 1.00 0.11 35
2 0.00 0.00 0.00 266
3 0.00 0.00 0.00 29
accuracy 0.28 800
macro avg 0.26 0.35 0.17 800
weighted avg 0.59 0.28 0.34 800
| precision | recall | f1-score | support | |
|---|---|---|---|---|
| 0 | 1.000000 | 0.40000 | 0.571429 | 470.00000 |
| 1 | 0.057190 | 1.00000 | 0.108192 | 35.00000 |
| 2 | 0.000000 | 0.00000 | 0.000000 | 266.00000 |
| 3 | 0.000000 | 0.00000 | 0.000000 | 29.00000 |
| accuracy | 0.278750 | 0.27875 | 0.278750 | 0.27875 |
| macro avg | 0.264297 | 0.35000 | 0.169905 | 800.00000 |
| weighted avg | 0.590002 | 0.27875 | 0.340448 | 800.00000 |
k-means Confusion Matrix:
k-means Agreggated Peformance Metrics:
| Metric | Value | |
|---|---|---|
| 0 | Precision (Macro) | 0.264297 |
| 1 | Recall (Macro) | 0.350000 |
| 2 | F1 Score (Macro) | 0.169905 |
| 3 | Precision (Weighted) | 0.590002 |
| 4 | Recall (Weighted) | 0.278750 |
| 5 | F1 Score (Weighted) | 0.340448 |
| 6 | Accuracy | 0.278750 |
| 7 | Overall Model Accuracy | 0.278750 |
Overall Model Accuracy : 0.27875
Best performing model: RandomForestClassifier(max_depth=15, n_estimators=200, random_state=42) Best model metric: 0
{'Precision (Macro)': 0.9295778807706288,
'Recall (Macro)': 0.8906330849133105,
'F1 Score (Macro)': 0.9087199423383634,
'Precision (Weighted)': 0.9721153671392222,
'Recall (Weighted)': 0.9725,
'F1 Score (Weighted)': 0.9719914504355294,
'Accuracy': 0.9725,
'Overall Model Accuracy ': 0.9725}
Best model saved to: /content/drive/My Drive/Model deployment/RandomForest_best_model.pkl
Model Development(improved version): Train all the models and setect the best one¶
In [ ]:
# unified_pipeline.py
import os
import joblib
import numpy as np
import pandas as pd
from collections import Counter, defaultdict
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, IsolationForest
from sklearn.neighbors import LocalOutlierFactor
from sklearn.svm import OneClassSVM
from sklearn.cluster import DBSCAN, KMeans
import tensorflow as tf
from tensorflow.keras import Model, Input
from tensorflow.keras.layers import Dense, LSTM, Reshape, Dropout
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping
# Define RANDOM_STATE for reproducibility
RANDOM_STATE = 42
# -------------------------
# Utilities
# -------------------------
def ensure_numpy(x):
return np.asarray(x) if not isinstance(x, np.ndarray) else x
def multiclass_metrics(y_true, y_pred):
y_true = ensure_numpy(y_true)
y_pred = ensure_numpy(y_pred)
return {
"Overall Model Accuracy": float(accuracy_score(y_true, y_pred)),
"Precision (Macro)": float(precision_score(y_true, y_pred, average="macro", zero_division=0)),
"Recall (Macro)": float(recall_score(y_true, y_pred, average="macro", zero_division=0)),
"F1 Score (Macro)": float(f1_score(y_true, y_pred, average="macro", zero_division=0))
}
def map_clusters_to_labels(train_clusters, train_labels):
"""
Given cluster labels or binary outputs on the training set and the true training labels,
return a dict mapping cluster_value -> majority Threat Level in that cluster.
"""
mapping = {}
df = pd.DataFrame({"cluster": train_clusters, "label": train_labels})
for cluster_val, group in df.groupby("cluster"):
most_common = group["label"].mode()
mapping[cluster_val] = int(most_common.iloc[0]) if not most_common.empty else int(group["label"].iloc[0])
return mapping
def apply_mapping(preds, mapping, default_label=0):
"""Map predicted cluster/binary values to multiclass labels using mapping dict."""
mapped = [mapping.get(p, default_label) for p in preds]
return np.array(mapped, dtype=int)
# -------------------------
# Unsupervised model wrappers (produce cluster/binary preds)
# -------------------------
def iso_forest_train_and_map(X_train, y_train, X_test):
model = IsolationForest(contamination=0.05, random_state=RANDOM_STATE)
model.fit(X_train)
raw_train = model.decision_function(X_train) # decision function as score
raw_test = model.decision_function(X_test)
raw_train_bin = np.where(model.predict(X_train) == -1, 1, 0) # -1 anomaly, 1 normal
raw_test_bin = np.where(model.predict(X_test) == -1, 1, 0)
mapping = map_clusters_to_labels(raw_train_bin, y_train)
mapped_test = apply_mapping(raw_test_bin, mapping, default_label=int(Counter(y_train).most_common(1)[0][0]))
X_test_viz = X_test.copy()
X_test_viz['anomaly_score'] = raw_test
X_test_viz['is_anomaly'] = raw_test_bin
return mapped_test, model, mapping, X_test_viz
def lof_train_and_map(X_train, y_train, X_test):
model = LocalOutlierFactor(n_neighbors=20, novelty=True)
model.fit(X_train)
raw_train = model.decision_function(X_train) # decision function as score
raw_test = model.decision_function(X_test)
raw_train_bin = np.where(model.predict(X_train) == -1, 1, 0) # -1 anomaly, 1 normal
raw_test_bin = np.where(model.predict(X_test) == -1, 1, 0)
mapping = map_clusters_to_labels(raw_train_bin, y_train)
mapped_test = apply_mapping(raw_test_bin, mapping, default_label=int(Counter(y_train).most_common(1)[0][0]))
X_test_viz = X_test.copy()
X_test_viz['anomaly_score'] = raw_test
X_test_viz['is_anomaly'] = raw_test_bin
return mapped_test, model, mapping, X_test_viz
def ocsvm_train_and_map(X_train, y_train, X_test):
model = OneClassSVM(kernel="rbf", gamma="auto", nu=0.05)
model.fit(X_train)
raw_train = model.decision_function(X_train) # decision function as score
raw_test = model.decision_function(X_test)
raw_train_bin = np.where(model.predict(X_train) == -1, 1, 0) # -1 anomaly, 1 normal
raw_test_bin = np.where(model.predict(X_test) == -1, 1, 0)
mapping = map_clusters_to_labels(raw_train_bin, y_train)
mapped_test = apply_mapping(raw_test_bin, mapping, default_label=int(Counter(y_train).most_common(1)[0][0]))
X_test_viz = X_test.copy()
X_test_viz['anomaly_score'] = raw_test
X_test_viz['is_anomaly'] = raw_test_bin
return mapped_test, model, mapping, X_test_viz
def dbscan_train_and_map(X_train, y_train, X_test):
model = DBSCAN(eps=0.5, min_samples=5)
train_clusters = model.fit_predict(X_train)
# DBSCAN labels -1 for noise (outliers)
test_clusters = model.fit_predict(X_test) # using fit_predict to create model on test (DBSCAN isn't typically used with separate test fit)
# *Note*: DBSCAN typically doesn't fit on train/test split; this is a pragmatic mapping approach
mapping = map_clusters_to_labels(train_clusters, y_train)
mapped_test = apply_mapping(test_clusters, mapping, default_label=int(Counter(y_train).most_common(1)[0][0]))
X_test_viz = X_test.copy()
# DBSCAN doesn't have a standard 'score', use -1 or distance to nearest core sample if needed.
# For visualization, let's use the cluster label itself, or a binary flag for noise (-1).
X_test_viz['anomaly_score'] = test_clusters # Use cluster label as score placeholder
X_test_viz['is_anomaly'] = (test_clusters == -1).astype(int) # Binary flag for noise
return mapped_test, model, mapping, X_test_viz
def kmeans_train_and_map(X_train, y_train, X_test, n_clusters=4):
# choose n_clusters default 4 (matching Threat Levels) but user can override
model = KMeans(n_clusters=n_clusters, random_state=RANDOM_STATE)
model.fit(X_train)
train_clusters = model.predict(X_train)
test_clusters = model.predict(X_test)
mapping = map_clusters_to_labels(train_clusters, y_train)
mapped_test = apply_mapping(test_clusters, mapping, default_label=int(Counter(y_train).most_common(1)[0][0]))
X_test_viz = X_test.copy()
# KMeans score could be distance to the assigned centroid
test_distances = np.linalg.norm(X_test - model.cluster_centers_[test_clusters], axis=1)
# Define 'anomaly' based on distance threshold or mapping.
# For visualization, let's use the distance as score and a simple threshold for binary flag.
# Threshold could be based on training data distances or a fixed percentile.
train_distances = np.linalg.norm(X_train - model.cluster_centers_[train_clusters], axis=1)
distance_threshold = np.percentile(train_distances, 95) # Example threshold
X_test_viz['anomaly_score'] = test_distances
X_test_viz['is_anomaly'] = (test_distances > distance_threshold).astype(int)
return mapped_test, model, mapping, X_test_viz
def autoencoder_train_and_map(X_train_np, y_train_np, X_test_np, X_test_columns, encoding_dim=None, epochs=30, batch_size=32):
# Ensure inputs are numpy arrays
X_train_np = ensure_numpy(X_train_np)
y_train_np = ensure_numpy(y_train_np)
X_test_np = ensure_numpy(X_test_np)
n_features = X_train_np.shape[1]
if encoding_dim is None:
encoding_dim = max(4, n_features // 2)
# simple dense autoencoder
inp = Input(shape=(n_features,))
x = Dense(64, activation='relu')(inp) # Adding hidden layers to Autoencoder
x = Dense(32, activation='relu')(x)
x = Dense(16, activation='relu')(x)
encoded = Dense(encoding_dim, activation="relu")(x) # Renamed to encoded
x = Dense(16, activation='relu')(encoded) # Adding hidden layers to Decoder
x = Dense(32, activation='relu')(x)
x = Dense(64, activation='relu')(x)
decoded = Dense(n_features, activation="sigmoid")(x) # Output layer
ae = Model(inp, decoded)
ae.compile(optimizer=Adam(1e-3), loss="mse")
early = EarlyStopping(monitor="val_loss", patience=5, restore_best_weights=True)
# Fit on numpy arrays
ae.fit(X_train_np, X_train_np, validation_data=(X_test_np, X_test_np), epochs=epochs, batch_size=batch_size, callbacks=[early], verbose=0)
recon_train = ae.predict(X_train_np, verbose=0)
mse_train = np.mean(np.square(X_train_np - recon_train), axis=1)
thresh = np.percentile(mse_train, 95) # threshold from train distribution
recon_test = ae.predict(X_test_np, verbose=0)
mse_test = np.mean(np.square(X_test_np - recon_test), axis=1)
raw_test_bin = np.where(mse_test > thresh, 1, 0)
train_bin = np.where(mse_train > thresh, 1, 0)
# Map using original y_train (assuming it's a pandas Series or can be converted)
mapping = map_clusters_to_labels(train_bin, y_train_np)
mapped_test = apply_mapping(raw_test_bin, mapping, default_label=int(Counter(y_train_np).most_common(1)[0][0]))
# Create X_test_viz as a DataFrame using the provided column names
X_test_viz = pd.DataFrame(X_test_np, columns=X_test_columns)
X_test_viz['anomaly_score'] = mse_test
X_test_viz['is_anomaly'] = raw_test_bin
return mapped_test, ae, mapping, mse_test, X_test_viz
# -------------------------
# Supervised model wrappers (predict multiclass directly)
# -------------------------
def rf_train_and_predict(X_train, y_train, X_test):
model = RandomForestClassifier(random_state=RANDOM_STATE)
model.fit(X_train, y_train)
preds = model.predict(X_test)
return preds, model, X_test.copy() # Return copy of X_test for consistency
def gb_train_and_predict(X_train, y_train, X_test):
model = GradientBoostingClassifier(random_state=RANDOM_STATE)
model.fit(X_train, y_train)
preds = model.predict(X_test)
return preds, model, X_test.copy() # Return copy of X_test for consistency
# -------------------------
# LSTM multiclass classifier
# -------------------------
def lstm_classifier_train_and_predict(X_train_np, y_train_np, X_test_np, X_test_columns, timesteps=1, epochs=30, batch_size=32):
"""
- timesteps: if >1, n_features must be divisible by timesteps and the arrays will be reshaped.
- y_train must be integer class labels [0..3].
"""
X_train_np = ensure_numpy(X_train_np)
y_train_np = ensure_numpy(y_train_np)
X_test_np = ensure_numpy(X_test_np)
n_features = X_train_np.shape[1]
if n_features % timesteps != 0:
raise ValueError("n_features must be divisible by timesteps when timesteps>1")
feat_per_step = n_features // timesteps
X_train_seq = X_train_np.reshape((X_train_np.shape[0], timesteps, feat_per_step))
X_test_seq = X_test_np.reshape((X_test_np.shape[0], timesteps, feat_per_step))
n_classes = len(np.unique(y_train_np))
y_train_cat = tf.keras.utils.to_categorical(y_train_np, num_classes=n_classes)
inputs = Input(shape=(timesteps, feat_per_step))
x = LSTM(64, activation='tanh')(inputs)
x = Dropout(0.2)(x)
outputs = Dense(n_classes, activation='softmax')(x)
model = Model(inputs, outputs)
model.compile(optimizer=Adam(1e-3), loss='categorical_crossentropy', metrics=['accuracy'])
early = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)
model.fit(X_train_seq, y_train_cat, validation_split=0.1, epochs=epochs, batch_size=batch_size, callbacks=[early], verbose=0)
preds_proba = model.predict(X_test_seq, verbose=0)
preds = np.argmax(preds_proba, axis=1)
# Create X_test_viz as a DataFrame using the provided column names
X_test_viz = pd.DataFrame(X_test_np, columns=X_test_columns)
# Supervised models don't have inherent 'anomaly_score' or 'is_anomaly' in the same way
# Add placeholder columns or decide on a different visualization strategy for these models
X_test_viz['anomaly_score'] = preds_proba[:, 1] if n_classes > 1 else 0 # Example: probability of class 1
X_test_viz['is_anomaly'] = (preds > 0).astype(int) # Example: predicted class > 0 is anomaly
return preds, model, X_test_viz
# -------------------------
# orchestrator: trains all models and selects best by accuracy
# -------------------------
def model_development_pipeline(data_path=None, df=None, target_col="Threat Level", test_size=0.2, random_state=42,
deploy_folder=".", lstm_timesteps=1):
"""
Provide either data_path (CSV) or df (pandas DataFrame). target_col must exist and be labeled 0..3.
Returns dict with model results and saves metrics CSV and best model to deploy_folder.
"""
# Load data
if df is None:
if data_path is None:
raise ValueError("Provide either df or data_path")
augmented_df = pd.read_csv(data_path)
else:
augmented_df = df.copy()
# Ensure target exists and numeric
if target_col not in augmented_df.columns:
raise ValueError(f"{target_col} missing from dataframe")
# ensure integer labels 0..3
augmented_df[target_col] = augmented_df[target_col].astype(int)
X = augmented_df.drop(columns=[target_col])
y = augmented_df[target_col]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state, stratify=y)
# Keep track of original column names for creating visualization DataFrames
original_columns = X.columns
results = {}
metrics_rows = []
# 1) Supervised classical
preds_rf, rf_model, X_test_rf_viz = rf_train_and_predict(X_train, y_train, X_test)
metrics_rf = multiclass_metrics(y_test, preds_rf)
results["RandomForest"] = {"model": rf_model, "preds": preds_rf, "metrics": metrics_rf, "X_test_viz": X_test_rf_viz}
metrics_rows.append({"model": "RandomForest", **metrics_rf})
preds_gb, gb_model, X_test_gb_viz = gb_train_and_predict(X_train, y_train, X_test)
metrics_gb = multiclass_metrics(y_test, preds_gb)
results["GradientBoosting"] = {"model": gb_model, "preds": preds_gb, "metrics": metrics_gb, "X_test_viz": X_test_gb_viz}
metrics_rows.append({"model": "GradientBoosting", **metrics_gb})
# 2) Unsupervised mapping -> multiclass
preds_iso, iso_model, iso_map, X_test_iso_viz = iso_forest_train_and_map(X_train, y_train, X_test)
metrics_iso = multiclass_metrics(y_test, preds_iso)
results["IsolationForest"] = {"model": iso_model, "preds": preds_iso, "metrics": metrics_iso, "mapping": iso_map, "X_test_viz": X_test_iso_viz}
metrics_rows.append({"model": "IsolationForest", **metrics_iso})
preds_ocsvm, ocsvm_model, ocsvm_map, X_test_ocsvm_viz = ocsvm_train_and_map(X_train, y_train, X_test)
metrics_ocsvm = multiclass_metrics(y_test, preds_ocsvm)
results["OneClassSVM"] = {"model": ocsvm_model, "preds": preds_ocsvm, "metrics": metrics_ocsvm, "mapping": ocsvm_map, "X_test_viz": X_test_ocsvm_viz}
metrics_rows.append({"model": "OneClassSVM", **metrics_ocsvm})
preds_lof, lof_model, lof_map, X_test_lof_viz = lof_train_and_map(X_train, y_train, X_test)
metrics_lof = multiclass_metrics(y_test, preds_lof)
results["LocalOutlierFactor"] = {"model": lof_model, "preds": preds_lof, "metrics": metrics_lof, "mapping": lof_map, "X_test_viz": X_test_lof_viz}
metrics_rows.append({"model": "LocalOutlierFactor", **metrics_lof})
# DBSCAN (note: DBSCAN doesn't naturally support separate test set; we attempt an approach for mapping)
try:
preds_dbscan, dbscan_model, dbscan_map, X_test_dbscan_viz = dbscan_train_and_map(X_train, y_train, X_test)
metrics_dbscan = multiclass_metrics(y_test, preds_dbscan)
except Exception as e:
preds_dbscan = np.full(len(y_test), int(Counter(y_train).most_common(1)[0][0]))
dbscan_model, dbscan_map, X_test_dbscan_viz = None, {}, X_test.copy()
metrics_dbscan = multiclass_metrics(y_test, preds_dbscan)
results["DBSCAN"] = {"model": dbscan_model, "preds": preds_dbscan, "metrics": metrics_dbscan, "mapping": dbscan_map, "X_test_viz": X_test_dbscan_viz}
metrics_rows.append({"model": "DBSCAN", **metrics_dbscan})
# KMeans
try:
preds_kmeans, kmeans_model, kmeans_map, X_test_kmeans_viz = kmeans_train_and_map(X_train, y_train, X_test, n_clusters=4)
metrics_kmeans = multiclass_metrics(y_test, preds_kmeans)
except Exception as e:
preds_kmeans = np.full(len(y_test), int(Counter(y_train).most_common(1)[0][0]))
kmeans_model, kmeans_map, X_test_kmeans_viz = None, {}, X_test.copy()
metrics_kmeans = multiclass_metrics(y_test, preds_kmeans)
results["KMeans"] = {"model": kmeans_model, "preds": preds_kmeans, "metrics": metrics_kmeans, "mapping": kmeans_map, "X_test_viz": X_test_kmeans_viz}
metrics_rows.append({"model": "KMeans", **metrics_kmeans})
# Autoencoder (dense)
# Pass X_test.values and X.columns to autoencoder_train_and_map
preds_ae, ae_model, ae_map, ae_scores, X_test_ae_viz = autoencoder_train_and_map(X_train.values, y_train.values, X_test.values, original_columns, epochs=30)
metrics_ae = multiclass_metrics(y_test, preds_ae)
results["Autoencoder"] = {"model": ae_model, "preds": preds_ae, "metrics": metrics_ae, "mapping": ae_map, "scores": ae_scores, "X_test_viz": X_test_ae_viz}
metrics_rows.append({"model": "Autoencoder", **metrics_ae})
# LSTM classifier (multiclass supervised)
# Pass X_test.values and X.columns to lstm_classifier_train_and_predict
try:
preds_lstm, lstm_model, X_test_lstm_viz = lstm_classifier_train_and_predict(X_train.values, y_train.values, X_test.values, original_columns, timesteps=lstm_timesteps, epochs=30)
metrics_lstm = multiclass_metrics(y_test, preds_lstm)
except Exception as e:
preds_lstm = np.full(len(y_test), int(Counter(y_train).most_common(1)[0][0]))
lstm_model, X_test_lstm_viz = None, X_test.copy()
metrics_lstm = multiclass_metrics(y_test, preds_lstm)
results["LSTM(Classifier)"] = {"model": lstm_model, "preds": preds_lstm, "metrics": metrics_lstm, "X_test_viz": X_test_lstm_viz}
metrics_rows.append({"model": "LSTM(Classifier)", **metrics_lstm})
# -------------------------
# Save metrics summary CSV
# -------------------------
metrics_df = pd.DataFrame(metrics_rows)
metrics_csv_path = os.path.join(deploy_folder, "model_metrics_summary.csv")
metrics_df.to_csv(metrics_csv_path, index=False)
# -------------------------
# Select best model by Overall Model Accuracy
# -------------------------
best_row = metrics_df.sort_values(by="Overall Model Accuracy", ascending=False).iloc[0]
best_model_name = best_row["model"]
best_metrics = results[best_model_name]["metrics"]
best_model_obj = results[best_model_name]["model"]
print(f"Best model selected by Overall Model Accuracy: {best_model_name} -> Accuracy: {best_metrics['Overall Model Accuracy']:.4f}")
# -------------------------
# Deploy best model
# -------------------------
os.makedirs(deploy_folder, exist_ok=True)
model_path = None
try:
if best_model_obj is None:
print("Best model object is None; nothing to save.")
model_path = None
else:
if hasattr(best_model_obj, "predict") and not isinstance(best_model_obj, tf.keras.Model):
# sklearn-like model -> joblib
model_path = os.path.join(deploy_folder, f"{best_model_name}_best_model.joblib")
joblib.dump(best_model_obj, model_path)
else:
# assume TF model
model_path = os.path.join(deploy_folder, f"{best_model_name}_best_model_tf")
best_model_obj.save(model_path, overwrite=True, include_optimizer=False)
print(f"Saved best model to: {model_path}")
except Exception as e:
print("Failed to save best model:", e)
model_path = None
pipeline_result = {
"results": results,
"metrics_df": metrics_df,
"best_model_name": best_model_name,
"best_model_obj": best_model_obj,
"best_model_path": model_path,
"y_test": y_test,
"X_test": X_test, # Include original X_test
"X_train": X_train, # Include original X_train
"y_train": y_train # Include original y_train
}
return pipeline_result
# print performance metrics charts
def print_model_metrics_charts(df):
metrics_to_plot = ['Overall Model Accuracy', 'Precision (Macro)', 'Recall (Macro)', 'F1 Score (Macro)']
num_metrics = len(metrics_to_plot)
# Create subplots: 2 rows, 2 columns for 4 metrics
fig = make_subplots(rows=2, cols=2, subplot_titles=metrics_to_plot)
# Define a list of colors for the bars in each subplot
colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728'] # Example colors
for i, metric in enumerate(metrics_to_plot):
if metric in df.columns:
# Sort data for each metric
sorted_df = df.sort_values(by=metric, ascending=False)
# Add bar trace to the corresponding subplot
row = (i // 2) + 1
col = (i % 2) + 1
fig.add_trace(go.Bar(
x=sorted_df['model'],
y=sorted_df[metric],
name=metric, # Name for legend (optional in subplots)
marker_color=colors[i] # Use a different color for each metric
), row=row, col=col)
# Update layout for the subplot axes if needed
fig.update_xaxes(title_text='Model', row=row, col=col)
fig.update_yaxes(title_text=metric, row=row, col=col)
else:
print(f"Metric '{metric}' not found in metrics_df.")
# Update overall layout
fig.update_layout(height=700, width=900, title_text="Model Performance Metrics", showlegend=False)
fig.show()
def main_model_delopement_function():
# Example usage: replace with your GDrive CSV path or pass df directly
pipeline = model_development_pipeline(data_path="/content/drive/My Drive/Cybersecurity Data/x_y_augmented_data_google_drive.csv",
target_col="Threat Level", deploy_folder="/content/drive/My Drive/Model deployment",
lstm_timesteps=1)
# Make metrics_df globally available
global metrics_df
metrics_df = pipeline["metrics_df"]
best_model_name = pipeline["best_model_name"]
best_model = pipeline["results"][best_model_name]["model"] # Get model object from results
y_pred = pipeline["results"][best_model_name]["preds"]
y_test = pipeline["y_test"]
X_test = pipeline["X_test"] # Get original X_test
metrics = pipeline["results"][best_model_name]["metrics"]
best_model_metric = metrics["Overall Model Accuracy"]
print("\nmetrics_df")
display(metrics_df)
print_model_metrics_charts(metrics_df)
print(f"Best model selected by Overall Model Accuracy: {best_model_name} -> Accuracy: {best_model_metric:.4f}")
print(pipeline["best_model_path"])
# Print classification report and confusion matrix for best model
print(f"\n{best_model_name} classification_report:")
print(classification_report(y_test, y_pred))
print_model_performance_report(best_model_name, y_test, y_pred)
# Print aggregated performance metrics for the best model
print(f"\n{best_model_name} Agreggated Peformance Metrics:")
best_model_metrics_df = pd.DataFrame([metrics]).T.reset_index()
best_model_metrics_df.columns = ['Metric', 'Value']
display(best_model_metrics_df)
print(f"\nOverall Model Accuracy : {best_model_metric}")
print("\n Model Performance Visualisation")
# Check if the best model is one of the unsupervised models that provides visualization data
if best_model_name in ["IsolationForest", "OneClassSVM", "LocalOutlierFactor", "DBSCAN", "KMeans", "Autoencoder"]:
# Retrieve the augmented X_test DataFrame for the best model
X_test_for_viz = pipeline["results"][best_model_name].get("X_test_viz")
if X_test_for_viz is not None:
# Visualize using the augmented DataFrame with generic anomaly columns
visualizing_model_performance_pipeline(
data=X_test_for_viz,
x="Session Duration in Second",
y="Data Transfer MB",
anomaly_score="anomaly_score", # Use generic column name
is_anomaly="is_anomaly", # Use generic column name
title="Model Performance Visualization\n"
)
else:
print(f"Visualization data (X_test_viz) not available for {best_model_name}.")
print("Skipping detailed anomaly visualization for this model.")
elif best_model_name in ["RandomForest", "GradientBoosting", "LSTM(Classifier)"]:
# Supervised models don't produce anomaly scores/flags in the same way.
# You might visualize actual vs predicted labels here, or skip this specific anomaly visualization.
# For now, we'll skip the anomaly visualization that expects 'anomaly_score' and 'is_anomaly'.
print(f"Visualization for model type '{best_model_name}' might require specific handling.")
print("The default anomaly visualization expects 'anomaly_score' and 'is_anomaly' columns, which supervised models do not typically produce.")
print("Skipping detailed anomaly visualization for this model.")
else:
print(f"Unknown best model type '{best_model_name}'. Cannot determine visualization strategy.")
print("\nModel development pipeline completed.")
# -------------------------
# If run as script - example
# -------------------------
if __name__ == "__main__":
main_model_delopement_function()
Best model selected by Overall Model Accuracy: GradientBoosting -> Accuracy: 0.9750 Saved best model to: /content/drive/My Drive/Model deployment/GradientBoosting_best_model.joblib metrics_df
| model | Overall Model Accuracy | Precision (Macro) | Recall (Macro) | F1 Score (Macro) | |
|---|---|---|---|---|---|
| 0 | RandomForest | 0.97125 | 0.948203 | 0.850529 | 0.885826 |
| 1 | GradientBoosting | 0.97500 | 0.975568 | 0.855430 | 0.898838 |
| 2 | IsolationForest | 0.59125 | 0.147813 | 0.250000 | 0.185782 |
| 3 | OneClassSVM | 0.59125 | 0.147813 | 0.250000 | 0.185782 |
| 4 | LocalOutlierFactor | 0.59125 | 0.147813 | 0.250000 | 0.185782 |
| 5 | DBSCAN | 0.59125 | 0.147813 | 0.250000 | 0.185782 |
| 6 | KMeans | 0.83875 | 0.411914 | 0.439837 | 0.425371 |
| 7 | Autoencoder | 0.59125 | 0.147813 | 0.250000 | 0.185782 |
| 8 | LSTM(Classifier) | 0.84875 | 0.418728 | 0.442517 | 0.429840 |
Best model selected by Overall Model Accuracy: GradientBoosting -> Accuracy: 0.9750
/content/drive/My Drive/Model deployment/GradientBoosting_best_model.joblib
GradientBoosting classification_report:
precision recall f1-score support
0 0.98 0.99 0.99 473
1 1.00 0.55 0.71 29
2 0.96 1.00 0.98 273
3 0.96 0.88 0.92 25
accuracy 0.97 800
macro avg 0.98 0.86 0.90 800
weighted avg 0.98 0.97 0.97 800
GradientBoosting classification_report:
precision recall f1-score support
0 0.98 0.99 0.99 473
1 1.00 0.55 0.71 29
2 0.96 1.00 0.98 273
3 0.96 0.88 0.92 25
accuracy 0.97 800
macro avg 0.98 0.86 0.90 800
weighted avg 0.98 0.97 0.97 800
| precision | recall | f1-score | support | |
|---|---|---|---|---|
| 0 | 0.981211 | 0.993658 | 0.987395 | 473.000 |
| 1 | 1.000000 | 0.551724 | 0.711111 | 29.000 |
| 2 | 0.964539 | 0.996337 | 0.980180 | 273.000 |
| 3 | 0.956522 | 0.880000 | 0.916667 | 25.000 |
| accuracy | 0.975000 | 0.975000 | 0.975000 | 0.975 |
| macro avg | 0.975568 | 0.855430 | 0.898838 | 800.000 |
| weighted avg | 0.975431 | 0.975000 | 0.972707 | 800.000 |
GradientBoosting Confusion Matrix:
GradientBoosting Agreggated Peformance Metrics:
| Metric | Value | |
|---|---|---|
| 0 | Precision (Macro) | 0.975568 |
| 1 | Recall (Macro) | 0.855430 |
| 2 | F1 Score (Macro) | 0.898838 |
| 3 | Precision (Weighted) | 0.975431 |
| 4 | Recall (Weighted) | 0.975000 |
| 5 | F1 Score (Weighted) | 0.972707 |
| 6 | Accuracy | 0.975000 |
| 7 | Overall Model Accuracy | 0.975000 |
Overall Model Accuracy : 0.975 GradientBoosting Agreggated Peformance Metrics:
| Metric | Value | |
|---|---|---|
| 0 | Overall Model Accuracy | 0.975000 |
| 1 | Precision (Macro) | 0.975568 |
| 2 | Recall (Macro) | 0.855430 |
| 3 | F1 Score (Macro) | 0.898838 |
Overall Model Accuracy : 0.975 Model Performance Visualisation Visualization for model type 'GradientBoosting' might require specific handling. The default anomaly visualization expects 'anomaly_score' and 'is_anomaly' columns, which supervised models do not typically produce. Skipping detailed anomaly visualization for this model. Model development pipeline completed.
Model Development (Version 3)Stacked Supervised Model using Unsupervised Anomaly Features¶
Implementation: Use Unsupervised Models as Feature Generators, and then trains a stacked supervised pipeline with:
- Base learner: Random Forest
- Meta learner: Gradient Boosting Classifier
The script:
Loads an augmented numeric dataset (assumes
Threat Levelencoded as integers 0..3).Standardizes features.
Trains the unsupervised models and extracts continuous anomaly features for train and test:
- Isolation Forest (decision function)
- One-Class SVM (decision function)
- Local Outlier Factor (decision function,
novelty=True) - DBSCAN (noise flag mapped to anomaly; test assignment via nearest neighbor to core samples)
- KMeans (distance to assigned centroid)
- Dense Autoencoder (reconstruction MSE)
- LSTM Autoencoder (reconstruction MSE; uses sequences with timestep=1)
Concatenates anomaly features with original normalized features.
Trains Random Forest as base model; collects
predict_probaon train/test.Trains Gradient Boosting meta-learner on stacked features (original+anomaly+RF-proba).
Evaluates the final stacked model (classification report, confusion matrix).
Saves models and scaler.
Notes¶
- Preprocessing: This script assumes the input
Xis numeric and preprocessed (no missing values, categorical features encoded). If you have categorical columns, one-hot or ordinal encode them before scaling. - Autoencoder/LSTM training set: I train autoencoders on a "normal" subset (
y_train <= 1) by default. Change that selection if your normal label mapping is different. - DBSCAN test assignment: DBSCAN does not natively predict new points; I've assigned test labels by nearest neighbor to the training samples' DBSCAN labels. This is a pragmatic solution; alternatives exist (re-fit on combined or use clustering methods that support predict).
- Hyperparameters: Tweak
CONTAMINATION,DBSCAN_EPS,KMEANS_CLUSTERS, epochs, and model hyperparameters for your dataset. - Compute time: Autoencoders and LSTM training can be slower; reduce epochs for quick experimentation.
- Interpretability: After training, examine feature importances from the Random Forest and Gradient Boosting models to see which anomaly features contributed most to improving predictions.
In [ ]:
"""
Fixed stacked supervised model using unsupervised anomaly features.
Key updates:
- Save/load Keras models in native .keras format
- Save train_X_scaled for DBSCAN nearest-neighbour assignment at inference
- Add inference-only anomaly feature extractor (no retraining)
- Safer AE training and checks
"""
import os
import numpy as np
import pandas as pd
import joblib
import json
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, IsolationForest
from sklearn.svm import OneClassSVM
from sklearn.neighbors import LocalOutlierFactor, NearestNeighbors
from sklearn.cluster import DBSCAN, KMeans
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
import tensorflow as tf
from tensorflow.keras.models import Model, load_model
from tensorflow.keras.layers import Input, Dense, LSTM, RepeatVector
from tensorflow.keras.callbacks import EarlyStopping
# ---------------------------
# PARAMETERS (adjust as needed)
# ---------------------------
DATA_PATH = "/content/drive/My Drive/Cybersecurity Data/x_y_augmented_data_google_drive.csv"
LABEL_COL = "Threat Level"
TEST_SIZE = 0.20
RANDOM_STATE = 42
AUTOENCODER_EPOCHS = 50
LSTM_EPOCHS = 50
AUTOENCODER_BATCH = 32
LSTM_BATCH = 32
DBSCAN_EPS = 0.5
DBSCAN_MIN_SAMPLES = 5
KMEANS_CLUSTERS = 4
CONTAMINATION = 0.05
MODEL_OUTPUT_DIR = "/content/drive/My Drive/stacked_models_deployment"
os.makedirs(MODEL_OUTPUT_DIR, exist_ok=True)
# Reproducibility
np.random.seed(RANDOM_STATE)
tf.random.set_seed(RANDOM_STATE)
def log(msg):
print(f"[INFO] {msg}")
with open(os.path.join(MODEL_OUTPUT_DIR, "log.txt"), "a") as f:
f.write(f"{msg}\n")
def build_dense_autoencoder(input_dim):
inp = Input(shape=(input_dim,))
x = Dense(64, activation='relu')(inp)
x = Dense(32, activation='relu')(x)
x = Dense(16, activation='relu')(x)
x = Dense(32, activation='relu')(x)
x = Dense(64, activation='relu')(x)
out = Dense(input_dim, activation='linear')(x)
model = Model(inputs=inp, outputs=out)
# explicit loss object to be safe when serializing
model.compile(optimizer='adam', loss=tf.keras.losses.MeanSquaredError())
return model
def build_lstm_autoencoder(timesteps, features):
inputs = Input(shape=(timesteps, features))
encoded = LSTM(128, activation='relu', return_sequences=False)(inputs)
decoded = RepeatVector(timesteps)(encoded)
decoded = LSTM(features, activation='linear', return_sequences=True)(decoded)
model = Model(inputs, decoded)
model.compile(optimizer='adam', loss=tf.keras.losses.MeanSquaredError())
return model
def load_dataset(path):
if not os.path.exists(path):
raise FileNotFoundError(f"Dataset not found: {path}")
df = pd.read_csv(path)
if LABEL_COL not in df.columns:
raise ValueError(f"Label column '{LABEL_COL}' not found in dataset.")
# ensure integer labels
df[LABEL_COL] = df[LABEL_COL].astype(int)
X = df.drop(columns=[LABEL_COL])
y = df[LABEL_COL]
return X, y
def extract_anomaly_features(X_train_scaled, X_test_scaled, y_train):
"""
Train unsupervised detectors on X_train_scaled and produce anomaly features for train/test.
Returns features_train (DataFrame), features_test (DataFrame), unsupervised_models (dict).
unsupervised_models will include 'train_X' stored as numpy array to support inference mapping.
"""
features_train = pd.DataFrame(index=np.arange(X_train_scaled.shape[0]))
features_test = pd.DataFrame(index=np.arange(X_test_scaled.shape[0]))
# Isolation Forest
log("Fitting IsolationForest...")
iso = IsolationForest(contamination=CONTAMINATION, random_state=RANDOM_STATE)
iso.fit(X_train_scaled)
features_train['iso_df'] = iso.decision_function(X_train_scaled)
features_test['iso_df'] = iso.decision_function(X_test_scaled)
# One-Class SVM
log("Fitting One-Class SVM...")
ocsvm = OneClassSVM(nu=CONTAMINATION, kernel='rbf', gamma='scale')
ocsvm.fit(X_train_scaled)
features_train['ocsvm_df'] = ocsvm.decision_function(X_train_scaled)
features_test['ocsvm_df'] = ocsvm.decision_function(X_test_scaled)
# Local Outlier Factor (novelty True so we can use decision_function/predict)
log("Fitting Local Outlier Factor (LOF)...")
lof = LocalOutlierFactor(n_neighbors=20, contamination=CONTAMINATION, novelty=True)
lof.fit(X_train_scaled)
features_train['lof_df'] = lof.decision_function(X_train_scaled)
features_test['lof_df'] = lof.decision_function(X_test_scaled)
# DBSCAN anomaly flag with nearest neighbor assignment for test set
log("Running DBSCAN clustering...")
db = DBSCAN(eps=DBSCAN_EPS, min_samples=DBSCAN_MIN_SAMPLES)
db_labels_train = db.fit_predict(X_train_scaled) # labels for training samples
# nearest neighbor mapping from test samples -> nearest train index
nbrs = NearestNeighbors(n_neighbors=1).fit(X_train_scaled)
nn_idx = nbrs.kneighbors(X_test_scaled, return_distance=False)[:, 0]
assigned_train_labels = db_labels_train[nn_idx]
features_train['dbscan_anomaly'] = (db_labels_train == -1).astype(float)
features_test['dbscan_anomaly'] = (assigned_train_labels == -1).astype(float)
# KMeans distances to cluster centers
log("Running KMeans clustering...")
kmeans = KMeans(n_clusters=KMEANS_CLUSTERS, random_state=RANDOM_STATE)
kmeans.fit(X_train_scaled)
train_k_labels = kmeans.predict(X_train_scaled)
test_k_labels = kmeans.predict(X_test_scaled)
train_distances = np.linalg.norm(X_train_scaled - kmeans.cluster_centers_[train_k_labels], axis=1)
test_distances = np.linalg.norm(X_test_scaled - kmeans.cluster_centers_[test_k_labels], axis=1)
features_train['kmeans_dist'] = train_distances
features_test['kmeans_dist'] = test_distances
# Dense Autoencoder reconstruction error
log("Training Dense Autoencoder...")
input_dim = X_train_scaled.shape[1]
dense_ae = build_dense_autoencoder(input_dim)
# Define "normal" mask (update threshold to suit your label encoding)
normal_mask = (y_train <= 1).to_numpy() if hasattr(y_train, "to_numpy") else (y_train <= 1)
X_ae_train = X_train_scaled[normal_mask]
# Only use validation_split if we have enough samples
fit_kwargs = {"epochs": AUTOENCODER_EPOCHS, "batch_size": AUTOENCODER_BATCH, "verbose": 0,
"callbacks": [EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)]}
if len(X_ae_train) > 50:
fit_kwargs["validation_split"] = 0.1
dense_ae.fit(X_ae_train, X_ae_train, **fit_kwargs)
features_train['ae_mse'] = np.mean((X_train_scaled - dense_ae.predict(X_train_scaled, verbose=0)) ** 2, axis=1)
features_test['ae_mse'] = np.mean((X_test_scaled - dense_ae.predict(X_test_scaled, verbose=0)) ** 2, axis=1)
# LSTM Autoencoder reconstruction error (reshape sequences with timesteps=1)
log("Training LSTM Autoencoder...")
timesteps = 1
X_train_seq = X_train_scaled.reshape((X_train_scaled.shape[0], timesteps, input_dim))
X_test_seq = X_test_scaled.reshape((X_test_scaled.shape[0], timesteps, input_dim))
lstm_ae = build_lstm_autoencoder(timesteps, input_dim)
fit_kwargs_lstm = {"epochs": LSTM_EPOCHS, "batch_size": LSTM_BATCH, "verbose": 0,
"callbacks": [EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)]}
if len(X_ae_train) > 50:
fit_kwargs_lstm["validation_split"] = 0.1
lstm_ae.fit(X_train_seq[normal_mask], X_train_seq[normal_mask], **fit_kwargs_lstm)
features_train['lstm_mse'] = np.mean((X_train_seq - lstm_ae.predict(X_train_seq, verbose=0)) ** 2, axis=(1, 2))
features_test['lstm_mse'] = np.mean((X_test_seq - lstm_ae.predict(X_test_seq, verbose=0)) ** 2, axis=(1, 2))
unsupervised_models = {
'iso': iso, 'ocsvm': ocsvm, 'lof': lof, 'dbscan': db,
'kmeans': kmeans, 'dense_ae': dense_ae, 'lstm_ae': lstm_ae,
'train_X': np.asarray(X_train_scaled) # save training X scaled for inference mapping
}
return features_train, features_test, unsupervised_models
def extract_anomaly_features_inference(X_scaled, unsupervised_models):
"""
Use trained unsupervised models (and saved train_X) to compute anomaly features for new X_scaled.
Does NOT retrain any models.
"""
features = pd.DataFrame(index=np.arange(X_scaled.shape[0]))
iso = unsupervised_models['iso']
ocsvm = unsupervised_models['ocsvm']
lof = unsupervised_models['lof']
db = unsupervised_models['dbscan']
kmeans = unsupervised_models['kmeans']
dense_ae = unsupervised_models['dense_ae']
lstm_ae = unsupervised_models['lstm_ae']
train_X = unsupervised_models.get('train_X', None)
if train_X is None:
raise ValueError("Missing 'train_X' in unsupervised_models; needed for DBSCAN assignment.")
# IsolationForest
features['iso_df'] = iso.decision_function(X_scaled)
# One-Class SVM
features['ocsvm_df'] = ocsvm.decision_function(X_scaled)
# LOF (novelty=True required for decision_function on new data)
features['lof_df'] = lof.decision_function(X_scaled)
# DBSCAN assignment using nearest neighbor to training samples
nbrs = NearestNeighbors(n_neighbors=1).fit(train_X)
nn_idx = nbrs.kneighbors(X_scaled, return_distance=False)[:, 0]
db_labels_train = db.labels_
assigned_train_labels = db_labels_train[nn_idx]
features['dbscan_anomaly'] = (assigned_train_labels == -1).astype(float)
# KMeans distance to cluster centers
k_labels = kmeans.predict(X_scaled)
k_dist = np.linalg.norm(X_scaled - kmeans.cluster_centers_[k_labels], axis=1)
features['kmeans_dist'] = k_dist
# Dense AE MSE
features['ae_mse'] = np.mean((X_scaled - dense_ae.predict(X_scaled, verbose=0)) ** 2, axis=1)
# LSTM AE MSE (reshape)
timesteps = 1
input_dim = X_scaled.shape[1]
X_seq = X_scaled.reshape((X_scaled.shape[0], timesteps, input_dim))
features['lstm_mse'] = np.mean((X_seq - lstm_ae.predict(X_seq, verbose=0)) ** 2, axis=(1, 2))
return features
def save_scaler_and_models(output_dir, scaler, base_model, meta_model, unsupervised_models):
os.makedirs(output_dir, exist_ok=True)
joblib.dump(scaler, os.path.join(output_dir, "scaler.joblib"))
joblib.dump(base_model, os.path.join(output_dir, "rf_base.joblib"))
joblib.dump(meta_model, os.path.join(output_dir, "gb_meta.joblib"))
# save classical unsupervised models
for name in ['iso', 'ocsvm', 'lof', 'dbscan', 'kmeans']:
joblib.dump(unsupervised_models[name], os.path.join(output_dir, f"{name}.joblib"))
# save train_X_scaled for DBSCAN mapping
np.save(os.path.join(output_dir, "train_X_scaled.npy"), unsupervised_models['train_X'])
# save Keras models in native format (.keras)
dense_path = os.path.join(output_dir, "dense_autoencoder.keras")
lstm_path = os.path.join(output_dir, "lstm_autoencoder.keras")
unsupervised_models['dense_ae'].save(dense_path)
unsupervised_models['lstm_ae'].save(lstm_path)
log(f" Scaler and ALL models saved in '{output_dir}'")
#--------------------------
# Load Trained Features
#--------------------------
def load_treaned_features(scaler, input_data):
log("Loading treaned features...")
if isinstance(input_data, str):
if not os.path.exists(input_data):
raise FileNotFoundError(f"Input CSV file not found: {input_data}")
df = pd.read_csv(input_data)
elif isinstance(input_data, pd.DataFrame):
df = input_data.copy()
else:
raise TypeError("input_data must be a filepath or a pandas DataFrame.")
# Get training feature names from the scaler
trained_feature_names = list(scaler.feature_names_in_)
# Keep only the columns that were in training
X_new = df.copy()
X_new = X_new[[c for c in X_new.columns if c in trained_feature_names]]
# Add any missing columns (fill with 0 or training mean if available)
for col in trained_feature_names:
if col not in df.columns:
X_new[col] = 0 # or use scaler.mean_[trained_feature_names.index(col)] if you want means
# Reorder columns exactly as in training
X_new = X_new[trained_feature_names]
return X_new
def load_scaler_and_models(output_dir):
scaler = joblib.load(os.path.join(output_dir, "scaler.joblib"))
base_model = joblib.load(os.path.join(output_dir, "rf_base.joblib"))
meta_model = joblib.load(os.path.join(output_dir, "gb_meta.joblib"))
unsupervised_models = {}
for name in ['iso', 'ocsvm', 'lof', 'dbscan', 'kmeans']:
unsupervised_models[name] = joblib.load(os.path.join(output_dir, f"{name}.joblib"))
# load train_X_scaled
unsupervised_models['train_X'] = np.load(os.path.join(output_dir, "train_X_scaled.npy"))
unsupervised_models['dense_ae'] = load_model(os.path.join(output_dir, "dense_autoencoder.keras"))
unsupervised_models['lstm_ae'] = load_model(os.path.join(output_dir, "lstm_autoencoder.keras"))
return scaler, base_model, meta_model, unsupervised_models
def predict_new_data(input_data, model_dir=MODEL_OUTPUT_DIR):
log("Loading scaler and models for inference...")
scaler, base_model, meta_model, unsupervised_models = load_scaler_and_models(model_dir)
X_new = load_treaned_features(scaler, input_data)
#load_treaned_features(input_data)
log("Scaling input features...")
X_scaled = scaler.transform(X_new)
log("Extracting anomaly features on new data (inference mode)...")
anomaly_features = extract_anomaly_features_inference(X_scaled, unsupervised_models)
X_ext = pd.concat([pd.DataFrame(X_scaled, columns=X_new.columns).reset_index(drop=True),
anomaly_features.reset_index(drop=True)], axis=1)
base_proba = base_model.predict_proba(X_ext)
X_stack = np.hstack([X_ext.values, base_proba])
y_pred = meta_model.predict(X_stack)
y_proba = meta_model.predict_proba(X_stack)
log("Prediction complete.")
return y_pred, y_proba
def main():
log("Loading dataset...")
X, y = load_dataset(DATA_PATH)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=TEST_SIZE, stratify=y, random_state=RANDOM_STATE)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
anomaly_train_df, anomaly_test_df, unsupervised_models = extract_anomaly_features(
X_train_scaled, X_test_scaled, y_train
)
X_train_ext = pd.concat([pd.DataFrame(X_train_scaled, columns=X.columns), anomaly_train_df], axis=1)
X_test_ext = pd.concat([pd.DataFrame(X_test_scaled, columns=X.columns), anomaly_test_df], axis=1)
log("Training RandomForest base model...")
rf = RandomForestClassifier(n_estimators=200, random_state=RANDOM_STATE, n_jobs=-1)
rf.fit(X_train_ext, y_train)
rf_train_proba = rf.predict_proba(X_train_ext)
rf_test_proba = rf.predict_proba(X_test_ext)
X_train_stack = np.hstack([X_train_ext.values, rf_train_proba])
X_test_stack = np.hstack([X_test_ext.values, rf_test_proba])
log("Training GradientBoosting meta model...")
gb = GradientBoostingClassifier(n_estimators=200, random_state=RANDOM_STATE)
gb.fit(X_train_stack, y_train)
y_pred = gb.predict(X_test_stack)
acc = accuracy_score(y_test, y_pred)
report = classification_report(y_test, y_pred)
cm = confusion_matrix(y_test, y_pred)
log(f"Accuracy: {acc:.4f}")
#print(f"Accuracy: {acc:.4f}")
#print(f"Classification Report:\n{report}")
#print(f"Confusion Matrix:\n{cm}")
print_model_performance_report(type(gb).__name__, y_test, y_pred)
#visualizing_model_performance_pipeline(data, x, y, anomaly_score, is_anomaly, title=None)
# Save metrics to JSON
metrics_path = os.path.join(MODEL_OUTPUT_DIR, "metrics.json")
with open(metrics_path, "w") as f:
json.dump({"accuracy": acc, "classification_report": classification_report(y_test, y_pred, output_dict=True),
"confusion_matrix": cm.tolist()}, f, indent=4)
log(f"Saved evaluation metrics to {metrics_path}")
save_scaler_and_models(MODEL_OUTPUT_DIR, scaler, rf, gb, unsupervised_models)
if __name__ == "__main__":
main()
# example inference - adjust path as desired
#preds, probs = predict_new_data("/content/drive/My Drive/Cybersecurity Data/x_y_augmented_data_google_drive.csv")
#print("Predicted classes:", preds)
#print("Prediction probabilities:", probs)
[INFO] Loading dataset...
[INFO] Fitting IsolationForest...
[INFO] Fitting One-Class SVM...
[INFO] Fitting Local Outlier Factor (LOF)...
[INFO] Running DBSCAN clustering...
[INFO] Running KMeans clustering...
[INFO] Training Dense Autoencoder...
[INFO] Training LSTM Autoencoder...
[INFO] Training RandomForest base model...
[INFO] Training GradientBoosting meta model...
[INFO] Accuracy: 0.9600
GradientBoostingClassifier classification_report:
precision recall f1-score support
0 0.98 0.98 0.98 473
1 0.94 0.55 0.70 29
2 0.93 0.99 0.96 273
3 0.89 0.64 0.74 25
accuracy 0.96 800
macro avg 0.94 0.79 0.85 800
weighted avg 0.96 0.96 0.96 800
| precision | recall | f1-score | support | |
|---|---|---|---|---|
| 0 | 0.978947 | 0.983087 | 0.981013 | 473.00 |
| 1 | 0.941176 | 0.551724 | 0.695652 | 29.00 |
| 2 | 0.934483 | 0.992674 | 0.962700 | 273.00 |
| 3 | 0.888889 | 0.640000 | 0.744186 | 25.00 |
| accuracy | 0.960000 | 0.960000 | 0.960000 | 0.96 |
| macro avg | 0.935874 | 0.791871 | 0.845888 | 800.00 |
| weighted avg | 0.959590 | 0.960000 | 0.957018 | 800.00 |
GradientBoostingClassifier Confusion Matrix:
GradientBoostingClassifier Agreggated Peformance Metrics:
| Metric | Value | |
|---|---|---|
| 0 | Precision (Macro) | 0.935874 |
| 1 | Recall (Macro) | 0.791871 |
| 2 | F1 Score (Macro) | 0.845888 |
| 3 | Precision (Weighted) | 0.959590 |
| 4 | Recall (Weighted) | 0.960000 |
| 5 | F1 Score (Weighted) | 0.957018 |
| 6 | Accuracy | 0.960000 |
| 7 | Overall Model Accuracy | 0.960000 |
Overall Model Accuracy : 0.96 [INFO] Saved evaluation metrics to /content/drive/My Drive/stacked_models_deployment/metrics.json [INFO] Scaler and ALL models saved in '/content/drive/My Drive/stacked_models_deployment'
8. Best Model Testing Using Real time simulation¶
In [ ]:
def flag_anomaly(model, df, model_type, input_feature_column, target_column='Threat Level'):
# Supervised Models
if model_type in ["RandomForestClassifier", "GradientBoostingClassifier"]:
y_pred = model.predict(df[input_feature_column])
df["Pred Threat"] = y_pred
#model_preds = [1 if pred == -1 else 0 for pred in y_pred]
df["anomaly_score"] = y_pred
df["is_anomaly"] = y_pred == 1
return df
# Isolation Forest
elif model_type == "IsolationForest":
y_pred = model.predict(df[input_feature_column])
df["Pred Threat"] = y_pred
model_preds = [1 if pred == -1 else 0 for pred in y_pred]
df["anomaly_score"] = model_preds
df["is_anomaly"] = model_preds == 1
return df
# Autoencoder
elif model_type.lower() == "sequential": # assumes Keras Sequential model
reconstructed = model.predict(df[input_feature_column])
df["Pred Threat"] = reconstructed
reconstruction_error = np.mean(np.square(df[input_feature_column] - reconstructed), axis=1)
threshold = np.percentile(reconstruction_error, 95)
model_preds = [1 if error > threshold else 0 for error in reconstruction_error]
df["anomaly_score"] = model_preds
df["Autoencoder_is_anomaly"] = model_preds == 1
return df
# one Class SVM
elif model_type == "OneClassSVM":
y_preds = model.fit_predict(df[input_feature_column])
df["Pred Threat"] = y_preds
model_preds = [1 if pred == -1 else 0 for pred in y_preds]
df["anomaly_score"] = model_preds
df["is_anomaly"] = model_preds == 1
return df
# Local Outlier Factor
elif model_type == "LocalOutlierFactor":
y_preds = model.fit_predict(df[input_feature_column])
df["Pred_Threat"] = y_preds
model_preds = [1 if pred == -1 else 0 for pred in y_preds]
df["anomaly_score"] = model_preds
df["is_anomaly"] = model_preds == 1
return df
# DBSCAN
elif model_type == "DBSCAN":
y_preds = model.fit_predict(df[input_feature_column])
df["Pred Threat"] = y_preds
model_pred = np.where(y_pred == -1, 1, 0)
df["anomaly_score"] =model_pred
df["is_anomaly"] = model_pred == 1
return df
# LSTM (assuming a Keras LSTM model)
elif model_type.lower() == "functional": # for Keras LSTM with Functional API
y_preds = model.predict(df[input_feature_column])
df["Pred Threat"] = y_preds
mse = np.mean(np.power(df[input_feature_column] - y_preds, 2), axis=1)
threshold = np.percentile(mse, 95)
df["anomaly_score"] = mse
df["is_anomaly"] = df["anomaly_score"] > threshold
return df
# KMeans
elif model_type == "KMeans":
y_preds = model.fit_predict(df[input_feature_column])
df["Pred Threat"] = y_preds
distances = np.linalg.norm(df[input_feature_column] - model.cluster_centers_[y_preds], axis=1)
threshold = np.percentile(distances, 95)
model_preds = np.where(distances > threshold, 1, 0)
df["anomaly_score"] = model_preds
df["is_anomaly"] = df["anomaly_score"] == 1
return df
else:
raise ValueError(f"Unsupported model type: {model_type}")
#------------------------------------Save the DataFrame to a CSV file--------------------------------------
def save_dataframe_to_drive(df, save_path):
df.to_csv(save_path, index=False)
print(f"DataFrame saved to: {save_path}")
#--------------------------------------decode_features--------------------------------------------------
def decode_features(df, loaded_label_encoders, num_fe_scaler, features_engineering_columns):
# Decode categorical features
for col, encoder in loaded_label_encoders.items():
if col in df.columns: # Check if the column exists in the DataFrame
try:
df[col] = encoder.inverse_transform(df[col])
except ValueError as e:
print(f"Error decoding column '{col}': {e}")
# Handle the error appropriately (e.g., skip the column or fill with a default value)
# Inverse transform numerical features
if features_engineering_columns: # check if the list is not empty
numerical_cols = [col for col in features_engineering_columns if col in df.columns]
if numerical_cols: # Check if the list of numerical cols is not empty
try:
df[numerical_cols] = num_fe_scaler.inverse_transform(df[numerical_cols])
except ValueError as e:
print(f"Error decoding numerical features: {e}")
print(f"\nloaded_label_encoders: {loaded_label_encoders}")
print(f"\features_engineering_columns: {features_engineering_columns}")
display(df)
return df
#-----------------------------------Best Model Testing Main Pipeline-----------------------------------------------
def best_model_testing_main_pipeline():
file_production_data_path = "/content/drive/My Drive/Cybersecurity Data/x_y_augmented_data_google_drive.csv"
model_path = "/content/drive/My Drive/Model deployment/RandomForest_best_model.pkl"
file_production_data_folder = "/content/drive/My Drive/Cybersecurity Data/"
# Load the dataset
file_path_to_normal_and_anomalous_google_drive = \
"/content/drive/My Drive/Cybersecurity Data/normal_and_anomalous_cybersecurity_dataset_for_google_drive_kb.csv"
df = pd.read_csv(file_path_to_normal_and_anomalous_google_drive)
display(df)
fe_processed_df, loaded_label_encoders, num_fe_scaler = load_objects_from_drive()
features_engineering_columns = fe_processed_df.columns.tolist()
input_feature_column = [col for col in features_engineering_columns if col != "Threat Level"]
target_column = "Threat Level"
features_engineering_columns.remove("Threat Level")
# Load the model
model = joblib.load(model_path)
model_type = type(model).__name__
#encode features using loaded_label_encoders and num_fe_scaler
for col, encoder in loaded_label_encoders.items():
df[col] = encoder.transform(df[col])
df[features_engineering_columns] = num_fe_scaler.transform(df[features_engineering_columns])
#rename threat level column name
#df.rename(columns={'Threat Level': 'Actual Threat'}, inplace=True)
#normal_and_anomalous_df = fe_processed_df.copy()
encode_normal_and_anomalous_flaged_df = flag_anomaly(model, df, model_type, input_feature_column, target_column='Threat Level')
display( encode_normal_and_anomalous_flaged_df.head())
print("\nencode_normal_and_anomalous_flaged_dfanomaly_score")
display( encode_normal_and_anomalous_flaged_df["anomaly_score"])
model_metrics_dic = print_model_performance_report(model_type, encode_normal_and_anomalous_flaged_df["Threat Level"],
encode_normal_and_anomalous_flaged_df["Pred Threat"])
visualizing_model_performance_pipeline(
data=encode_normal_and_anomalous_flaged_df,
x="Session Duration in Second",
y="Data Transfer MB",
anomaly_score="anomaly_score", # Use model_type to construct column name
is_anomaly="is_anomaly", # Use model_type to construct column name
title="Model Performance Visualization\n"
)
#decode features using loaded_label_encoders and num_fe_scaler
normal_and_anomalous_flaged_df = decode_features(encode_normal_and_anomalous_flaged_df,
loaded_label_encoders,
num_fe_scaler,
features_engineering_columns)
#save normal_and_anomalous_df to google drive
save_dataframe_to_drive(normal_and_anomalous_flaged_df, file_production_data_folder+"normal_and_anomalous_flaged_df.csv")
if __name__ == "__main__":
best_model_testing_main_pipeline()
| Issue ID | Issue Key | Issue Name | Issue Volume | Category | Severity | Status | Reporters | Assignees | Date Reported | ... | Session Duration in Second | Num Files Accessed | Login Attempts | Data Transfer MB | CPU Usage % | Memory Usage MB | Threat Score | Threat Level | Defense Action | Color | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ISSUE-0001 | KEY-0001 | Unauthorized Access Leading to Data Exposure | 1 | Data Breach | Low | Closed | Reporter 7 | Assignee 16 | 2023-12-07 | ... | 1002 | 26 | 6 | 3420.0 | 34.417556 | 7717 | 9.682 | Critical | Increase Monitoring & Schedule Review | Lock A... | Orange |
| 1 | ISSUE-0002 | KEY-0002 | Increased Exposure due to Insufficient Data En... | 1 | Risk Exposure | Low | In Progress | Reporter 1 | Assignee 4 | 2023-05-05 | ... | 1649 | 26 | 9 | 2825.0 | 38.368115 | 7828 | 14.314 | Critical | Increase Monitoring & Schedule Review | Lock A... | Orange |
| 2 | ISSUE-0003 | KEY-0003 | Non-Compliance with Data Protection Regulations | 1 | Legal Compliance | Medium | Closed | Reporter 3 | Assignee 6 | 2024-05-03 | ... | 2190 | 26 | 6 | 1022.5 | 21.429354 | 4263 | 18.496 | Critical | Isolate Affected System & Restrict User Access... | Orange-Red |
| 3 | ISSUE-0004 | KEY-0004 | Insufficient Coverage in Annual Risk Assessment | 1 | Risk Assessment Coverage | Low | Resolved | Reporter 3 | Assignee 17 | 2025-06-22 | ... | 907 | 36 | 18 | 2692.5 | 33.896298 | 6366 | 15.352 | Critical | Increase Monitoring & Schedule Review | Lock A... | Orange |
| 4 | ISSUE-0005 | KEY-0005 | Inconsistent Review of Security Policies | 1 | Management Oversight | High | In Progress | Reporter 7 | Assignee 13 | 2024-03-28 | ... | 900 | 42 | 3 | 3122.0 | 53.059948 | 5927 | 18.902 | Critical | Escalate to Security Operations Center (SOC) &... | Red |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1595 | ISSUE-0996 | KEY-0996 | Outdated Operating System Components | 1 | System Vulnerability | Medium | Resolved | Reporter 3 | Assignee 20 | 2024-07-31 | ... | 1825 | 26 | 9 | 11332.5 | 40.313911 | 3765 | 21.514 | Critical | Isolate Affected System & Restrict User Access... | Orange-Red |
| 1596 | ISSUE-0997 | KEY-0997 | Non-Compliance with Data Protection Regulations | 1 | Legal Compliance | Critical | In Progress | Reporter 10 | Assignee 1 | 2024-10-24 | ... | 1234 | 28 | 6 | 8291.0 | 53.128825 | 5903 | 17.646 | Critical | Immediate System-wide Shutdown & Investigation... | Dark Red |
| 1597 | ISSUE-0998 | KEY-0998 | Missing or Inaccurate Asset Records | 1 | Asset Inventory Accuracy | Critical | Open | Reporter 9 | Assignee 3 | 2025-01-01 | ... | 1649 | 31 | 10 | 8792.0 | 68.930727 | 3495 | 13.544 | Critical | Immediate System-wide Shutdown & Investigation... | Dark Red |
| 1598 | ISSUE-0999 | KEY-0999 | Insufficient Coverage in Annual Risk Assessment | 1 | Risk Assessment Coverage | High | In Progress | Reporter 8 | Assignee 20 | 2024-03-29 | ... | 1676 | 26 | 17 | 9707.0 | 20.165971 | 4749 | 29.638 | Critical | Escalate to Security Operations Center (SOC) &... | Red |
| 1599 | ISSUE-1000 | KEY-1000 | Delayed Patching of Known Vulnerabilities | 1 | Vulnerability Remediation | Low | In Progress | Reporter 10 | Assignee 7 | 2023-03-09 | ... | 1369 | 26 | 6 | 2595.0 | 76.599668 | 4050 | 20.340 | Critical | Increase Monitoring & Schedule Review | Lock A... | Orange |
1600 rows × 33 columns
DataFrame loaded successfully from: /content/drive/My Drive/Cybersecurity Data/df_fe.pkl Label encoders loaded successfully from: /content/drive/My Drive/Model deployment/cat_cols_label_encoders.pkl Label encoders loaded successfully from: /content/drive/My Drive/Model deployment/ num_fe_scaler.pkl
| Issue ID | Issue Key | Issue Name | Issue Volume | Category | Severity | Status | Reporters | Assignees | Date Reported | ... | Data Transfer MB | CPU Usage % | Memory Usage MB | Threat Score | Threat Level | Defense Action | Color | Pred Threat | anomaly_score | is_anomaly | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 16 | 1 | 5 | 2 | 0 | 7 | 7 | 2023-12-07 | ... | 0.325896 | 0.317656 | 0.742516 | 0.230310 | 0 | 11 | 4 | 0 | 0 | False |
| 1 | 1 | 1 | 7 | 1 | 15 | 2 | 1 | 0 | 14 | 2023-05-05 | ... | 0.299197 | 0.364527 | 0.756472 | 0.378848 | 0 | 11 | 4 | 0 | 0 | False |
| 2 | 2 | 2 | 11 | 1 | 7 | 3 | 0 | 3 | 16 | 2024-05-03 | ... | 0.218316 | 0.163559 | 0.308246 | 0.512955 | 0 | 13 | 5 | 0 | 0 | False |
| 3 | 3 | 3 | 9 | 1 | 14 | 2 | 3 | 3 | 8 | 2025-06-22 | ... | 0.293252 | 0.311472 | 0.572655 | 0.412134 | 0 | 11 | 4 | 0 | 0 | False |
| 4 | 4 | 4 | 6 | 1 | 9 | 1 | 1 | 7 | 4 | 2024-03-28 | ... | 0.312524 | 0.538836 | 0.517460 | 0.525975 | 0 | 3 | 6 | 0 | 0 | False |
5 rows × 36 columns
encode_normal_and_anomalous_flaged_dfanomaly_score
| anomaly_score | |
|---|---|
| 0 | 0 |
| 1 | 0 |
| 2 | 0 |
| 3 | 0 |
| 4 | 0 |
| ... | ... |
| 1595 | 0 |
| 1596 | 0 |
| 1597 | 0 |
| 1598 | 0 |
| 1599 | 0 |
1600 rows × 1 columns
RandomForestClassifier classification_report:
precision recall f1-score support
0 1.00 1.00 1.00 1332
1 0.99 0.99 0.99 114
2 0.96 1.00 0.98 46
3 1.00 1.00 1.00 108
accuracy 1.00 1600
macro avg 0.99 1.00 0.99 1600
weighted avg 1.00 1.00 1.00 1600
| precision | recall | f1-score | support | |
|---|---|---|---|---|
| 0 | 0.999248 | 0.997748 | 0.998497 | 1332.0000 |
| 1 | 0.991228 | 0.991228 | 0.991228 | 114.0000 |
| 2 | 0.958333 | 1.000000 | 0.978723 | 46.0000 |
| 3 | 1.000000 | 1.000000 | 1.000000 | 108.0000 |
| accuracy | 0.997500 | 0.997500 | 0.997500 | 0.9975 |
| macro avg | 0.987202 | 0.997244 | 0.992112 | 1600.0000 |
| weighted avg | 0.997551 | 0.997500 | 0.997512 | 1600.0000 |
RandomForestClassifier Confusion Matrix:
RandomForestClassifier Agreggated Peformance Metrics:
| Metric | Value | |
|---|---|---|
| 0 | Precision (Macro) | 0.987202 |
| 1 | Recall (Macro) | 0.997244 |
| 2 | F1 Score (Macro) | 0.992112 |
| 3 | Precision (Weighted) | 0.997551 |
| 4 | Recall (Weighted) | 0.997500 |
| 5 | F1 Score (Weighted) | 0.997512 |
| 6 | Accuracy | 0.997500 |
| 7 | Overall Model Accuracy | 0.997500 |
Overall Model Accuracy : 0.9975
loaded_label_encoders: {'Issue ID': LabelEncoder(), 'Issue Key': LabelEncoder(), 'Issue Name': LabelEncoder(), 'Category': LabelEncoder(), 'Severity': LabelEncoder(), 'Status': LabelEncoder(), 'Reporters': LabelEncoder(), 'Assignees': LabelEncoder(), 'Risk Level': LabelEncoder(), 'Department Affected': LabelEncoder(), 'Remediation Steps': LabelEncoder(), 'KPI/KRI': LabelEncoder(), 'User ID': LabelEncoder(), 'Activity Type': LabelEncoder(), 'User Location': LabelEncoder(), 'IP Location': LabelEncoder(), 'Threat Level': LabelEncoder(), 'Defense Action': LabelEncoder(), 'Color': LabelEncoder()}
eatures_engineering_columns: ['Issue Response Time Days', 'Impact Score', 'Cost', 'Session Duration in Second', 'Num Files Accessed', 'Login Attempts', 'Data Transfer MB', 'CPU Usage %', 'Memory Usage MB', 'Threat Score']
| Issue ID | Issue Key | Issue Name | Issue Volume | Category | Severity | Status | Reporters | Assignees | Date Reported | ... | Data Transfer MB | CPU Usage % | Memory Usage MB | Threat Score | Threat Level | Defense Action | Color | Pred Threat | anomaly_score | is_anomaly | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ISSUE-0001 | KEY-0001 | Unauthorized Access Leading to Data Exposure | 1 | Data Breach | Low | Closed | Reporter 7 | Assignee 16 | 2023-12-07 | ... | 3420.0 | 34.417556 | 7717.0 | 9.682 | Critical | Increase Monitoring & Schedule Review | Lock A... | Orange | 0 | 0 | False |
| 1 | ISSUE-0002 | KEY-0002 | Increased Exposure due to Insufficient Data En... | 1 | Risk Exposure | Low | In Progress | Reporter 1 | Assignee 4 | 2023-05-05 | ... | 2825.0 | 38.368115 | 7828.0 | 14.314 | Critical | Increase Monitoring & Schedule Review | Lock A... | Orange | 0 | 0 | False |
| 2 | ISSUE-0003 | KEY-0003 | Non-Compliance with Data Protection Regulations | 1 | Legal Compliance | Medium | Closed | Reporter 3 | Assignee 6 | 2024-05-03 | ... | 1022.5 | 21.429354 | 4263.0 | 18.496 | Critical | Isolate Affected System & Restrict User Access... | Orange-Red | 0 | 0 | False |
| 3 | ISSUE-0004 | KEY-0004 | Insufficient Coverage in Annual Risk Assessment | 1 | Risk Assessment Coverage | Low | Resolved | Reporter 3 | Assignee 17 | 2025-06-22 | ... | 2692.5 | 33.896298 | 6366.0 | 15.352 | Critical | Increase Monitoring & Schedule Review | Lock A... | Orange | 0 | 0 | False |
| 4 | ISSUE-0005 | KEY-0005 | Inconsistent Review of Security Policies | 1 | Management Oversight | High | In Progress | Reporter 7 | Assignee 13 | 2024-03-28 | ... | 3122.0 | 53.059948 | 5927.0 | 18.902 | Critical | Escalate to Security Operations Center (SOC) &... | Red | 0 | 0 | False |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1595 | ISSUE-0996 | KEY-0996 | Outdated Operating System Components | 1 | System Vulnerability | Medium | Resolved | Reporter 3 | Assignee 20 | 2024-07-31 | ... | 11332.5 | 40.313911 | 3765.0 | 21.514 | Critical | Isolate Affected System & Restrict User Access... | Orange-Red | 0 | 0 | False |
| 1596 | ISSUE-0997 | KEY-0997 | Non-Compliance with Data Protection Regulations | 1 | Legal Compliance | Critical | In Progress | Reporter 10 | Assignee 1 | 2024-10-24 | ... | 8291.0 | 53.128825 | 5903.0 | 17.646 | Critical | Immediate System-wide Shutdown & Investigation... | Dark Red | 0 | 0 | False |
| 1597 | ISSUE-0998 | KEY-0998 | Missing or Inaccurate Asset Records | 1 | Asset Inventory Accuracy | Critical | Open | Reporter 9 | Assignee 3 | 2025-01-01 | ... | 8792.0 | 68.930727 | 3495.0 | 13.544 | Critical | Immediate System-wide Shutdown & Investigation... | Dark Red | 0 | 0 | False |
| 1598 | ISSUE-0999 | KEY-0999 | Insufficient Coverage in Annual Risk Assessment | 1 | Risk Assessment Coverage | High | In Progress | Reporter 8 | Assignee 20 | 2024-03-29 | ... | 9707.0 | 20.165971 | 4749.0 | 29.638 | Critical | Escalate to Security Operations Center (SOC) &... | Red | 0 | 0 | False |
| 1599 | ISSUE-1000 | KEY-1000 | Delayed Patching of Known Vulnerabilities | 1 | Vulnerability Remediation | Low | In Progress | Reporter 10 | Assignee 7 | 2023-03-09 | ... | 2595.0 | 76.599668 | 4050.0 | 20.340 | Critical | Increase Monitoring & Schedule Review | Lock A... | Orange | 0 | 0 | False |
1600 rows × 36 columns
DataFrame saved to: /content/drive/My Drive/Cybersecurity Data/normal_and_anomalous_flaged_df.csv
Stacked Supervised Model using Unsupervised Anomaly Features - Testing and deployment Environnement¶
in this section we run the stacking model performance report on real time simulated data and perform reusage in production
- Save / deploy the stacked pipeline artifacts (scaler, base RF, meta GB and all unsupervised models + helper objects used at training time).
- Reload those artifacts in another process (or production service).
- Preprocess incoming real-time records the same way you did during training.
- Generate anomaly features from the saved unsupervised models for new data (single-record and batch).
- Predict the multiclass
Threat Levelusing the stacked pipeline (Random Forest base → Gradient Boosting meta).
The code assumes you saved models exactly as in the pipeline you previously ran (joblib for sklearn models, .h5 for Keras models). It also saves a few training-time helper objects required to make DBSCAN assignment robust (NearestNeighbors fitted on training X) and to reconstruct feature names.
explanation of the approach & important notes¶
What we save
scaler.joblib— keeps feature scaling consistent.rf_base.joblib— base Random Forest.gb_meta.joblib— meta Gradient Boosting.unsup_sklearn.joblib— all sklearn unsupervised models (IsolationForest, OCSVM, LOF, DBSCAN, KMeans).dense_autoencoder.h5,lstm_autoencoder.h5— Keras models.dbscan_train_X_scaled.joblib— training X used for nearest-neighbor assignment for DBSCAN, plusunsup_meta.joblibcontainingfeature_columns.
DBSCAN on new points
- DBSCAN cannot predict new points; we assign each incoming point to its nearest neighbor from training data and reuse that train-sample DBSCAN label. That's why we saved
dbscan_train_X_scaledand the fitted DBSCAN object'slabels_. This is a pragmatic approach — you might prefer to re-fit DBSCAN on a growing window if new data distribution shifts.
- DBSCAN cannot predict new points; we assign each incoming point to its nearest neighbor from training data and reuse that train-sample DBSCAN label. That's why we saved
LSTM / Autoencoder
- LSTM was trained as an autoencoder with
timesteps=1in the training pipeline. For inference, we reshape incoming single records to(1, 1, n_features)and compute reconstruction MSE. - If your production input is truly sequential, consider collecting small recent windows to feed LSTM (i.e., actual time-series sequences).
- LSTM was trained as an autoencoder with
Feature order & column names
feature_columnssaved in metadata ensures you reorder incoming data the same way training used it.
Batch vs single-record
- The code supports both. For real-time single-record scoring you can call
predict_realtime_single().
- The code supports both. For real-time single-record scoring you can call
Model updates
- If you re-train models, re-run
save_deployment_packagewith new artifacts and rotate models in production.
- If you re-train models, re-run
Performance & latency
Some unsupervised features (dense AE, LSTM) add compute cost. For very low-latency applications, consider:
- Using a lighter autoencoder
- Running heavy models in an async pipeline and using a fast fallback
- Precomputing anomaly features for frequent entities
In [ ]:
# --------------------------
# Necessary Imports
# --------------------------
import os
import pandas as pd
import numpy as np
import joblib
from tensorflow.keras.models import load_model
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
# --------------------------
# Configurations
# --------------------------
#Stacked Supervised Model using Unsupervised Anomaly Features
MODEL_TYPE = "Stacked Supervised Model using Unsupervised Anomaly Features"
MODEL_NAME = "Stacked_AD_classifier"
THREASHHOLD_PERC = 95
LABEL_COL = "Threat Level" # Ground truth label column name
MODELS_DIR = "/content/drive/My Drive/stacked_models_deployment"
SIMULATED_REAL_TIME_DATA_FILE = \
"/content/drive/My Drive/Cybersecurity Data/normal_and_anomalous_cybersecurity_dataset_for_google_drive_kb.csv"
# --------------------------
# Ensure model directory exists
# --------------------------
if not os.path.exists(MODEL_OUTPUT_DIR):
os.makedirs(MODEL_OUTPUT_DIR)
# --------------------------
# Logging Function
# --------------------------
def log(msg):
"""Logs a message to both console and a file in MODEL_OUTPUT_DIR."""
print(f"[INFO] {msg}")
with open(os.path.join(MODEL_OUTPUT_DIR, "log.txt"), "a") as f:
f.write(f"{msg}\n")
# --------------------------
# Check Required Model Files (Table View)
# --------------------------
def check_required_files(output_dir):
"""Checks if all model/scaler files exist before loading, shows table."""
required_files = [
"scaler.joblib", "rf_base.joblib", "gb_meta.joblib",
"iso.joblib", "ocsvm.joblib", "lof.joblib", "dbscan.joblib", "kmeans.joblib",
"train_X_scaled.npy", "dense_autoencoder.keras", "lstm_autoencoder.keras"
]
print("\n📂 Checking Required Model Files:\n" + "-" * 50)
missing_files = []
for f in required_files:
file_path = os.path.join(output_dir, f)
if os.path.exists(file_path):
print(f"✅ {f} — FOUND")
else:
print(f"❌ {f} — MISSING")
missing_files.append(f)
print("-" * 50)
if missing_files:
raise FileNotFoundError(f"\nMissing required model files:\n - " + "\n - ".join(missing_files))
# --------------------------
# Load Trained Features
# --------------------------
def load_treaned_features(scaler, input_data):
"""Ensures new data matches the trained feature set."""
log("Loading trained features...")
if isinstance(input_data, str):
if not os.path.exists(input_data):
raise FileNotFoundError(f"Input CSV file not found: {input_data}")
df = pd.read_csv(input_data)
elif isinstance(input_data, pd.DataFrame):
df = input_data.copy()
else:
raise TypeError("input_data must be a filepath or a pandas DataFrame.")
if LABEL_COL in df.columns:
df = df.drop(columns=[LABEL_COL])
trained_feature_names = list(scaler.feature_names_in_)
X_new = df[[c for c in df.columns if c in trained_feature_names]].copy()
for col in trained_feature_names:
if col not in X_new.columns:
X_new[col] = 0
X_new = X_new[trained_feature_names]
return X_new
# --------------------------
# Load Scaler and Models
# --------------------------
def load_scaler_and_models(output_dir):
"""Loads scaler, supervised models, and unsupervised models from output_dir."""
check_required_files(output_dir) # Ensure all files exist before loading
scaler = joblib.load(os.path.join(output_dir, "scaler.joblib"))
base_model = joblib.load(os.path.join(output_dir, "rf_base.joblib"))
meta_model = joblib.load(os.path.join(output_dir, "gb_meta.joblib"))
unsupervised_models = {}
for name in ['iso', 'ocsvm', 'lof', 'dbscan', 'kmeans']:
unsupervised_models[name] = joblib.load(os.path.join(output_dir, f"{name}.joblib"))
unsupervised_models['train_X'] = np.load(os.path.join(output_dir, "train_X_scaled.npy"))
unsupervised_models['dense_ae'] = load_model(os.path.join(output_dir, "dense_autoencoder.keras"))
unsupervised_models['lstm_ae'] = load_model(os.path.join(output_dir, "lstm_autoencoder.keras"))
return scaler, base_model, meta_model, unsupervised_models
#----------------------------------
# Encode Simulated Real Time Data
#----------------------------------
def encode_simulated_real_time_data(df_p, LABEL_COL):
df = df_p.copy()
fe_processed_df, loaded_label_encoders, num_fe_scaler = load_objects_from_drive()
features_engineering_columns = fe_processed_df.columns.tolist()
input_feature_column = [col for col in features_engineering_columns if col != LABEL_COL]
features_engineering_columns.remove(LABEL_COL)
#encode features using loaded_label_encoders and num_fe_scaler
for col, encoder in loaded_label_encoders.items():
df[col] = encoder.transform(df[col])
return df
# --------------------------
# Prediction Function
# --------------------------
def model_2SM2UAF_predict_anomaly_features_inference(encoded_df,
y_pred,
y_test,
LABEL_COL,
threashhold_perc = 95):
#encoded_df = encode_simulated_real_time_data(df, LABEL_COL)
#features_engineering_columns = df.columns.tolist()
#features_engineering_columns.remove(LABEL_COL)
#df[features_engineering_columns] = num_fe_scaler.transform(df[features_engineering_columns])
#threshold = np.percentile(y_probas, threashhold_perc)
#encoded_df["Pred Threat"] = y_pred
#--------------------------------
mse = np.mean(np.power(y_test - y_pred, 2))
threshold = np.percentile(mse, threashhold_perc)
encoded_df["anomaly_score"] = mse
encoded_df["is_anomaly"] = encoded_df["anomaly_score"] > threshold
#-----------------------------
#encoded_df["Pred Threat Probability"] = y_probas
#encoded_df["anomaly_score"] = y_probas
#ncoded_df["is_anomaly"] = encoded_df["anomaly_score"] > threshold
#------------------------------
#y_preds = model.predict(df[input_feature_column])
# df["Pred Threat"] = y_preds
# mse = np.mean(np.power(df[input_feature_column] - y_preds, 2), axis=1)
# threshold = np.percentile(mse, 95)
# df["anomaly_score"] = mse
#-----------------------------
return encoded_df
def predict_new_data(input_data, LABEL_COL, model_dir=MODELS_DIR):
"""Predicts on new data and evaluates if LABEL_COL exists."""
log("Loading scaler and models for inference...")
scaler, base_model, meta_model, unsupervised_models = load_scaler_and_models(model_dir)
#if isinstance(input_data, str):
# df_raw = pd.read_csv(input_data)
#elif isinstance(input_data, pd.DataFrame):
# df_raw = input_data.copy()
#else:
# raise TypeError("input_data must be a filepath or a pandas DataFrame.")
#encoded_df_raw = encode_simulated_real_time_data(df_raw, LABEL_COL)
#----------------------------------------------------
#features_engineering_columns = encoded_df_raw.columns.tolist()
#features_engineering_columns.remove(LABEL_COL)
if isinstance(input_data, str):
augmented_df, d_loss_real_list, d_loss_fake_list, g_loss_list = data_augmentation_pipeline(
file_path=input_data,
lead_save_true_false = False)
encoded_df_raw = augmented_df.copy()
else:
log("Initial data frame is empty...")
raise TypeError("input_data must be a filepath ")
#-------------------------------------------------------------------
y_test = encoded_df_raw[LABEL_COL] if LABEL_COL in encoded_df_raw.columns else None
X_new = load_treaned_features(scaler, encoded_df_raw)
log("Scaling input features...")
X_scaled = scaler.transform(X_new)
log("Extracting anomaly features...")
anomaly_features = extract_anomaly_features_inference(X_scaled, unsupervised_models)
X_ext = pd.concat(
[pd.DataFrame(X_scaled, columns=X_new.columns).reset_index(drop=True),
anomaly_features.reset_index(drop=True)],
axis=1
)
base_proba = base_model.predict_proba(X_ext)
X_stack = np.hstack([X_ext.values, base_proba])
y_pred = meta_model.predict(X_stack)
y_proba = meta_model.predict_proba(X_stack)
log("Prediction complete.")
if y_test is not None:
acc = accuracy_score(y_test, y_pred)
report = classification_report(y_test, y_pred)
#cm = confusion_matrix(y_test, y_pred)
#print(f"\nAccuracy: {acc:.4f}")
print("\nClassification Report:\n", report)
#print("\nConfusion Matrix:\n", cm)
df_raw_anomaly_pred = model_2SM2UAF_predict_anomaly_features_inference(encoded_df_raw,
y_pred,
y_test,
LABEL_COL,
THREASHHOLD_PERC)
model_metrics_dic = print_model_performance_report(MODEL_NAME, y_test, y_pred)
visualizing_model_performance_pipeline(
data=df_raw_anomaly_pred,
x="Session Duration in Second",
y="Data Transfer MB",
anomaly_score="anomaly_score", # Use model_type to construct column name
is_anomaly="is_anomaly", # Use model_type to construct column name
title="Model Performance Visualization\n"
)
return y_pred, y_proba
# This cell defines a function to format and display model inference output with dynamic insights.
def display_model_inference_output(preds, probs, class_names):
"""
Formats and displays model inference output (predicted classes and probabilities)
with dynamic explanations and business insights.
Args:
preds (np.ndarray): Array of predicted class labels.
probs (np.ndarray): Array of prediction probabilities.
class_names (dict): Mapping from numerical class labels to names.
"""
# --- Explanation and Business Insight ---
print("--- Model Prediction Output Analysis ---")
print("\nBased on the model's inference results:")
# Display the shape of the predicted classes array.
# This shows the total number of instances for which a class prediction was made.
num_instances = preds.shape[0]
print(f"\nShape of Predicted Classes: {preds.shape}")
print(f"Business Insight: The model processed and made predictions for a total of {num_instances} instances.")
# Display the shape of the prediction probabilities array.
# This shows the total number of instances and the number of classes (columns) with probability scores.
num_classes = probs.shape[1]
print(f"\nShape of Prediction Probabilities: {probs.shape}")
print(f"Business Insight: For each instance, the model provided a probability score for each of the {num_classes} possible threat levels.")
print("\n--- First 10 Predictions and Probabilities ---")
# Display the first 10 predicted class labels, including their names.
print("\nFirst 10 Predicted Classes (Numerical and Name):")
for i, pred in enumerate(preds[:10]):
print(f"Instance {i+1}: {pred} ({class_names.get(pred, 'Unknown Class')})")
# Display the first 10 rows of prediction probabilities, rounded for clarity.
# Each row shows the probability of the instance belonging to each of the 4 classes.
print("\nFirst 10 Prediction Probabilities:")
# Create a temporary DataFrame to display with column names
probs_df = pd.DataFrame(probs[:10], columns=[class_names.get(i, f'Class {i}') for i in range(probs.shape[1])])
display(np.round(probs_df, 4))
print("Business Insight: Examining the probabilities for individual instances shows the model's confidence in its predictions for specific events.")
print("\n--- Prediction Probability Summary Statistics ---")
# Display the average probability across all predictions and all classes.
avg_prob = np.mean(probs)
print(f"\nAverage Prediction Probability (across all classes and instances): {avg_prob:.4f}")
insight_avg_prob = f"An average probability around {1/num_classes:.2f} (for {num_classes} classes) might suggest a relatively balanced distribution of predictions or model uncertainty across classes. Further analysis of the probability distribution is recommended." if num_classes > 0 else "Cannot calculate average probability with 0 classes."
print(f"Business Insight: {insight_avg_prob}")
# Display the maximum probability assigned to any class for any instance.
max_prob = np.max(probs)
print(f"\nMaximum Prediction Probability (assigned to any class for any instance): {max_prob:.4f}")
insight_max_prob = "A maximum probability of 1.0 indicates the model is highly confident in some of its individual predictions." if max_prob == 1.0 else "The maximum probability is less than 1.0, suggesting the model has some level of uncertainty even in its most confident predictions."
print(f"Business Insight: {insight_max_prob}")
# Display the minimum probability assigned to any class for any instance.
min_prob = np.min(probs)
print(f"\nMinimum Prediction Probability (assigned to any class for any instance): {min_prob:.4f}")
insight_min_prob = "A minimum probability of 0.0 means the model is completely certain some instances do not belong to certain classes." if min_prob == 0.0 else "The minimum probability is greater than 0.0, suggesting the model assigns some non-zero probability to all classes for all instances."
print(f"Business Insight: {insight_min_prob}")
print("\n--- Overall Business Insight from Prediction Output ---")
print("""
This output provides a snapshot of the model's inference phase.
- The **shapes** confirm the total number of instances processed and the number of classes evaluated.
- The **predicted classes** indicate the primary threat level identified for each instance, enabling prioritized operational responses.
- The **prediction probabilities** offer a measure of the model's confidence. While high confidence in individual cases is good, the overall average probability suggests further investigation into the probability distribution and model uncertainty is valuable.
- To gain more specific business insights, analyze the distribution of predicted threat levels across all instances and investigate instances with lower confidence scores.
""")
# --------------------------
# Main Execution
# --------------------------
if __name__ == "__main__":
preds, probs = predict_new_data(SIMULATED_REAL_TIME_DATA_FILE, LABEL_COL)
# Log shapes
#print(f"\nPredicted classes shape: {preds.shape}")
#print(f"Prediction probabilities shape: {probs.shape}")
# Show first few predictions
#print("\nPredicted classes (first 10):", preds[:10])
#print("Prediction probabilities (first 10):\n", np.round(probs[:10], 4))
# Aggregate probability stats
#print(f"\nAverage probability: {np.mean(probs):.4f}")
#print(f"Max probability: {np.max(probs):.4f}")
#print(f"Min probability: {np.min(probs):.4f}")
class_names = {
0: 'Low',
1: 'Medium',
2: 'High',
3: 'Critical'
}
display_model_inference_output(preds, probs, class_names)
[INFO] Loading scaler and models for inference... 📂 Checking Required Model Files: -------------------------------------------------- ✅ scaler.joblib — FOUND ✅ rf_base.joblib — FOUND ✅ gb_meta.joblib — FOUND ✅ iso.joblib — FOUND ✅ ocsvm.joblib — FOUND ✅ lof.joblib — FOUND ✅ dbscan.joblib — FOUND ✅ kmeans.joblib — FOUND ✅ train_X_scaled.npy — FOUND ✅ dense_autoencoder.keras — FOUND ✅ lstm_autoencoder.keras — FOUND -------------------------------------------------- Feature engineering pipeline started. Anomaly Injection – Cholesky-Based Perturbation... Feature engineering pipeline completed. Data loaded from Google Drive. Balancing data with SMOTE...
Training GAN: 100%|██████████| 1000/1000 [03:33<00:00, 4.69it/s]
Data augmentation process complete.
[INFO] Loading trained features...
[INFO] Scaling input features...
[INFO] Extracting anomaly features...
[INFO] Prediction complete.
Classification Report:
precision recall f1-score support
0 1.00 1.00 1.00 2364
1 0.99 0.91 0.95 143
2 0.99 1.00 0.99 1364
3 0.98 0.93 0.96 126
accuracy 0.99 3997
macro avg 0.99 0.96 0.97 3997
weighted avg 0.99 0.99 0.99 3997
2SM2UAF_model classification_report:
precision recall f1-score support
0 1.00 1.00 1.00 2364
1 0.99 0.91 0.95 143
2 0.99 1.00 0.99 1364
3 0.98 0.93 0.96 126
accuracy 0.99 3997
macro avg 0.99 0.96 0.97 3997
weighted avg 0.99 0.99 0.99 3997
| precision | recall | f1-score | support | |
|---|---|---|---|---|
| 0 | 0.995773 | 0.996616 | 0.996195 | 2364.000000 |
| 1 | 0.992366 | 0.909091 | 0.948905 | 143.000000 |
| 2 | 0.986242 | 0.998534 | 0.992350 | 1364.000000 |
| 3 | 0.983193 | 0.928571 | 0.955102 | 126.000000 |
| accuracy | 0.991994 | 0.991994 | 0.991994 | 0.991994 |
| macro avg | 0.989394 | 0.958203 | 0.973138 | 3997.000000 |
| weighted avg | 0.992002 | 0.991994 | 0.991895 | 3997.000000 |
2SM2UAF_model Confusion Matrix:
2SM2UAF_model Agreggated Peformance Metrics:
| Metric | Value | |
|---|---|---|
| 0 | Precision (Macro) | 0.989394 |
| 1 | Recall (Macro) | 0.958203 |
| 2 | F1 Score (Macro) | 0.973138 |
| 3 | Precision (Weighted) | 0.992002 |
| 4 | Recall (Weighted) | 0.991994 |
| 5 | F1 Score (Weighted) | 0.991895 |
| 6 | Accuracy | 0.991994 |
| 7 | Overall Model Accuracy | 0.991994 |
Overall Model Accuracy : 0.9919939954966225
--- Model Prediction Output Analysis --- Based on the model's inference results: Shape of Predicted Classes: (3997,) Business Insight: The model processed and made predictions for a total of 3997 instances. Shape of Prediction Probabilities: (3997, 4) Business Insight: For each instance, the model provided a probability score for each of the 4 possible threat levels. --- First 10 Predictions and Probabilities --- First 10 Predicted Classes (Numerical and Name): Instance 1: 0 (Low) Instance 2: 0 (Low) Instance 3: 0 (Low) Instance 4: 0 (Low) Instance 5: 0 (Low) Instance 6: 0 (Low) Instance 7: 0 (Low) Instance 8: 0 (Low) Instance 9: 0 (Low) Instance 10: 0 (Low) First 10 Prediction Probabilities:
| Low | Medium | High | Critical | |
|---|---|---|---|---|
| 0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1 | 1.0 | 0.0 | 0.0 | 0.0 |
| 2 | 1.0 | 0.0 | 0.0 | 0.0 |
| 3 | 1.0 | 0.0 | 0.0 | 0.0 |
| 4 | 1.0 | 0.0 | 0.0 | 0.0 |
| 5 | 1.0 | 0.0 | 0.0 | 0.0 |
| 6 | 1.0 | 0.0 | 0.0 | 0.0 |
| 7 | 1.0 | 0.0 | 0.0 | 0.0 |
| 8 | 1.0 | 0.0 | 0.0 | 0.0 |
| 9 | 1.0 | 0.0 | 0.0 | 0.0 |
Business Insight: Examining the probabilities for individual instances shows the model's confidence in its predictions for specific events. --- Prediction Probability Summary Statistics --- Average Prediction Probability (across all classes and instances): 0.2500 Business Insight: An average probability around 0.25 (for 4 classes) might suggest a relatively balanced distribution of predictions or model uncertainty across classes. Further analysis of the probability distribution is recommended. Maximum Prediction Probability (assigned to any class for any instance): 1.0000 Business Insight: The maximum probability is less than 1.0, suggesting the model has some level of uncertainty even in its most confident predictions. Minimum Prediction Probability (assigned to any class for any instance): 0.0000 Business Insight: The minimum probability is greater than 0.0, suggesting the model assigns some non-zero probability to all classes for all instances. --- Overall Business Insight from Prediction Output --- This output provides a snapshot of the model's inference phase. - The **shapes** confirm the total number of instances processed and the number of classes evaluated. - The **predicted classes** indicate the primary threat level identified for each instance, enabling prioritized operational responses. - The **prediction probabilities** offer a measure of the model's confidence. While high confidence in individual cases is good, the overall average probability suggests further investigation into the probability distribution and model uncertainty is valuable. - To gain more specific business insights, analyze the distribution of predicted threat levels across all instances and investigate instances with lower confidence scores.
9. Cybersecurity Attack Simulation and Reporting¶
Attack Scenarios¶
In this session, we will simulate different cybersecurity attack scenarios such as phishing attack, laware infiltration, DDOS attack and data leak. Them we will implement an addaptative defense mechanism to mitigate the risk
- Phishing Attack: Increase login attempts and data transfer from anomalous IPs.
- Malware Infiltration: Abnormally high file access.
- DDOS Attack: Sudden surge in session duration and unusual locations.
- Data Leak: Abnormally high data transfer volumes.
Automated Defense Mechanisms:
- Lock accounts or restrict access when threat levels are high or critical.
- Escalate unresolved issues to SOC for immediate investigation.
- Automatically implement MFA requirements for specific behaviors.
Attack Data Consolidation¶
We will filter current year data and ddd behaviors such as spikes in login attempts, data transfer and file access during specific attacks:
In [ ]:
#from datetime import datetime
#import numpy as np
#import pandas as pd
# --- Utility Functions ---
def ensure_datetime(df, column):
df[column] = pd.to_datetime(df[column], errors='coerce')
return df.dropna(subset=[column])
def filter_by_year(df, column, year):
return df[df[column].dt.year == year]
# --- Attack Simulations ---
def simulate_phishing(df, verbose=False):
if verbose: print("[*] Simulating Phishing...")
targets = df[df["Category"] == "Access Control"].sample(frac=0.1)
df.loc[targets.index, "Login Attempts"] += np.random.randint(10, 20, len(targets))
df.loc[targets.index, "Impact Score"] += np.random.randint(10, 20, len(targets))
df.loc[targets.index, "Threat Score"] += np.random.randint(10, 20, len(targets))
return df
def simulate_malware(df, verbose=False):
if verbose: print("[*] Simulating Malware...")
targets = df[df["Category"] == "System Vulnerability"].sample(frac=0.1)
df.loc[targets.index, "Num Files Accessed"] += np.random.randint(50, 100, len(targets))
df.loc[targets.index, "Impact Score"] += np.random.randint(50, 100, len(targets))
df.loc[targets.index, "Threat Score"] += np.random.randint(50, 100, len(targets))
return df
def simulate_ddos(df, verbose=False):
if verbose: print("[*] Simulating DDoS...")
targets = df[df["Category"] == "Network Security"].sample(frac=0.1)
df.loc[targets.index, "Session Duration in Second"] += np.random.randint(10000, 20000, len(targets))
df.loc[targets.index, "Impact Score"] += np.random.randint(10000, 20000, len(targets))
df.loc[targets.index, "Threat Score"] += np.random.randint(10000, 20000, len(targets))
return df
def simulate_data_leak(df, verbose=False):
if verbose: print("[*] Simulating Data Leak...")
targets = df[df["Category"] == "Data Breach"].sample(frac=0.1)
df.loc[targets.index, "Data Transfer MB"] += np.random.uniform(500, 1000, len(targets))
df.loc[targets.index, "Impact Score"] += np.random.uniform(500, 1000, len(targets))
df.loc[targets.index, "Threat Score"] += np.random.uniform(500, 1000, len(targets))
return df
def simulate_insider_threat(df, verbose=False):
if verbose: print("[*] Simulating Insider Threat...")
df['hour'] = df['Timestamps'].dt.hour
late_hours = df[(df['hour'] < 5) | (df['hour'] > 23)]
targets = late_hours.sample(frac=0.1)
df.loc[targets.index, "Access Restricted Files"] = True
df.loc[targets.index, "Impact Score"] += np.random.randint(30, 60, len(targets))
df.loc[targets.index, "Threat Score"] += np.random.randint(30, 60, len(targets))
return df
def simulate_ransomware(df, verbose=False):
if verbose: print("[*] Simulating Ransomware...")
targets = df[df["Category"] == "System Vulnerability"].sample(frac=0.05)
df.loc[targets.index, "CPU Usage %"] += np.random.uniform(50, 80, len(targets))
df.loc[targets.index, "Memory Usage MB"] += np.random.uniform(1000, 3000, len(targets))
df.loc[targets.index, "Num Files Accessed"] += np.random.randint(200, 500, len(targets))
df.loc[targets.index, "Threat Score"] += np.random.randint(100, 200, len(targets))
df.loc[targets.index, "Impact Score"] += np.random.randint(100, 200, len(targets))
return df
#------------------------------------Save the DataFrame to a CSV file--------------------------------------
def save_dataframe_to_drive(df, save_path):
df.to_csv(save_path, index=False)
print(f"DataFrame saved to: {save_path}")
# --- Main Simulation Runner ---
def simulate_attack_scenarios(year_filter=None, attacks_to_simulate=None, verbose=True):
anomalous_flaged_production_df = "/content/drive/My Drive/Cybersecurity Data/normal_and_anomalous_flaged_df.csv"
file_production_data_folder = "/content/drive/My Drive/Cybersecurity Data/"
# Load the dataset
attack_df = pd.read_csv(anomalous_flaged_production_df)
attack_df = ensure_datetime(attack_df, "Timestamps")
if year_filter:
attack_df = filter_by_year(attack_df, "Timestamps", year_filter)
if verbose: print(f"[i] Filtering data for year {year_filter}...")
# Default to all if none specified
all_attacks = {
"phishing": simulate_phishing,
"malware": simulate_malware,
"ddos": simulate_ddos,
"data_leak": simulate_data_leak,
"insider": simulate_insider_threat,
"ransomware": simulate_ransomware
}
attacks_to_simulate = attacks_to_simulate or list(all_attacks.keys())
for attack_name in attacks_to_simulate:
func = all_attacks.get(attack_name.lower())
if func:
simulated_attacks_df = func(attack_df, verbose=verbose)
elif verbose:
print(f"[!] Unknown attack type: {attack_name}")
#return simulated_attacks_df
save_dataframe_to_drive(simulated_attacks_df, file_production_data_folder+"simulated_attacks_df.csv")
display(simulated_attacks_df.head())
return simulated_attacks_df
if __name__ == "__main__":
simulate_attack_scenarios()
[*] Simulating Phishing... [*] Simulating Malware... [*] Simulating DDoS... [*] Simulating Data Leak... [*] Simulating Insider Threat... [*] Simulating Ransomware... DataFrame saved to: /content/drive/My Drive/Cybersecurity Data/simulated_attacks_df.csv
| Issue ID | Issue Key | Issue Name | Issue Volume | Category | Severity | Status | Reporters | Assignees | Date Reported | ... | Memory Usage MB | Threat Score | Threat Level | Defense Action | Color | Pred Threat | anomaly_score | is_anomaly | hour | Access Restricted Files | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ISSUE-0001 | KEY-0001 | Unauthorized Access Leading to Data Exposure | 1 | Data Breach | Low | Closed | Reporter 7 | Assignee 16 | 2023-12-07 | ... | 7717.0 | 9.682 | Critical | Increase Monitoring & Schedule Review | Lock A... | Orange | 0 | 0 | False | 3 | NaN |
| 1 | ISSUE-0002 | KEY-0002 | Increased Exposure due to Insufficient Data En... | 1 | Risk Exposure | Low | In Progress | Reporter 1 | Assignee 4 | 2023-05-05 | ... | 7828.0 | 14.314 | Critical | Increase Monitoring & Schedule Review | Lock A... | Orange | 0 | 0 | False | 2 | NaN |
| 2 | ISSUE-0003 | KEY-0003 | Non-Compliance with Data Protection Regulations | 1 | Legal Compliance | Medium | Closed | Reporter 3 | Assignee 6 | 2024-05-03 | ... | 4263.0 | 18.496 | Critical | Isolate Affected System & Restrict User Access... | Orange-Red | 0 | 0 | False | 14 | NaN |
| 3 | ISSUE-0004 | KEY-0004 | Insufficient Coverage in Annual Risk Assessment | 1 | Risk Assessment Coverage | Low | Resolved | Reporter 3 | Assignee 17 | 2025-06-22 | ... | 6366.0 | 15.352 | Critical | Increase Monitoring & Schedule Review | Lock A... | Orange | 0 | 0 | False | 12 | NaN |
| 4 | ISSUE-0005 | KEY-0005 | Inconsistent Review of Security Policies | 1 | Management Oversight | High | In Progress | Reporter 7 | Assignee 13 | 2024-03-28 | ... | 5927.0 | 18.902 | Critical | Escalate to Security Operations Center (SOC) &... | Red | 0 | 0 | False | 9 | NaN |
5 rows × 38 columns
Executive Dashboard Summary¶
Summary report contents
- Threat Statistics:
- Total incidents categorized by severity and risk level.
- Percentage of incidents successfully mitigated by automated defenses.
- List of unresolved critical threats.
- Incident Details:
- Top 5 incidents by threat score.
- Actions taken against high-priority incidents.
- Performance Metrics:
- Average response time for incident resolution.
- Comparison of threat trends over the reporting period.
We will create a report to summarize the key metrics and export it as a PDF and CSV.
In [ ]:
def generate_executive_report(df):
# Threat statistics
total_theats = df.groupby("Threat Level").size()
severity_stats = df.groupby("Severity").size()
impact_cost_stats = round(df.groupby("Severity")["Cost"].sum()/ 1_000_000)
resolved_stats = df[df["Status"].isin(["Resolved", "Closed"])].groupby("Threat Level").size()
out_standing_issues = df[df["Status"].isin(["Open", "In Progress"])].groupby("Threat Level").size()
outstanding_issues_avg_resp_time = round(df[df["Status"].isin(["Open", "In Progress"])].groupby("Threat Level")["Issue Response Time Days"].mean())
solved_issues_avg_resp_time = round(df[df["Status"].isin(["Resolved", "Closed"])].groupby("Threat Level")["Issue Response Time Days"].mean())
# Top 5 issues
top_issues = df.nlargest(5, "Threat Score")
# Average response time
overall_avg_response_time = df["Issue Response Time Days"].mean()
# Save to CSV
report_summary_data_dic = {
"Total Attack": total_theats,
"Attack Volume Severity": severity_stats,
"Impact in Cost(M$)": impact_cost_stats,
"Resolved Issues": resolved_stats,
"Outstanding Issues": out_standing_issues,
"Outstanding Issues Avg Response Time": outstanding_issues_avg_resp_time,
"Solved Issues Avg Response Time": solved_issues_avg_resp_time,
"Top 5 Issues": top_issues.to_dict(),
"Overall Average Response Time(days)": overall_avg_response_time
}
top_five_issues_df = pd.DataFrame(report_summary_data_dic.pop("Top 5 Issues"))
top_five_issues_df["cost"] = top_five_issues_df["Cost"].apply(lambda x: round(x/1_000_000))
average_response_time = round(report_summary_data_dic.pop("Overall Average Response Time(days)"))
# Convert numeric columns to numeric type before creating the DataFrame
for col in ["Impact in Cost(M$)", "Outstanding Issues Avg Response Time", "Solved Issues Avg Response Time"]:
report_summary_data_dic[col] = pd.to_numeric(report_summary_data_dic[col], errors='coerce')
# Create report_summary_df from report_summary_data_dic
report_summary_df = pd.DataFrame(report_summary_data_dic)
# Apply round to numeric columns only after creating the DataFrame
report_summary_df = report_summary_df.apply(lambda x: round(x) if x.dtype.kind in 'biufc' else x)
top_five_incidents_defense_df = top_five_issues_df[["Issue ID", "Threat Level", "Severity",
"Issue Response Time Days", "Department Affected", "Cost", "Defense Action"]]
days = 184
hours = days * 24
minutes = days * 1440
average_response_time ={
"Average Response Time in days" : average_response_time,
"Average Response Time in hours" : hours,
"Average Response Time in minutes" : minutes
}
average_response_time_df = pd.DataFrame(average_response_time, index=[0])
print("\nreport_summary_df\n")
display(report_summary_df)
print("\naverage_response_time\n")
display(average_response_time_df)
print("\nTop 5 issues impact with Addaptative Defense Mechanism\n")
display(top_five_incidents_defense_df)
return report_summary_data_dic
#------------------------- Plot Executive Report metrics--------------------------------------------
#Bar chart--
def plot_executive_report_bars(data_dic):
# Define the number of plots
num_plots = len(data_dic)
# Create a figure with 2 rows and 4 columns
fig, axes = plt.subplots(2, 4, figsize=(20, 10), constrained_layout=True)
axes = axes.flatten() # Flatten the axes for easier indexing
# Define the colors for each plot
colors = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b", "#e377c2"]
# Iterate over the data dictionary and create each subplot
for i, (title, data) in enumerate(data_dic.items()):
if i >= len(axes): # Break if more plots than subplots
break
ax = axes[i]
# Sort data for ascending bars
sorted_data = data.sort_values()
# Plot the horizontal bar chart
ax.barh(sorted_data.index, sorted_data.values, color=colors[i % len(colors)])
# Customize the subplot
ax.set_title(title, fontsize=14)
ax.set_facecolor("#f5f5f5") # Light gray background
ax.spines['top'].set_visible(False) # Remove top border
ax.spines['right'].set_visible(False) # Remove right border
ax.spines['left'].set_visible(False) # Remove left border
ax.spines['bottom'].set_visible(False) # Remove bottom border
ax.xaxis.set_visible(False) # Hide the x-axis
for j, v in enumerate(sorted_data.values):
ax.text(v, j, str(v), va='center', fontsize=10) # Add labels
# Remove extra subplots if fewer data points
for i in range(num_plots, len(axes)):
fig.delaxes(axes[i])
# Display the plots
plt.show()
#donut chart---------------------
def plot_executive_report_donut_charts(data_dic):
# Define the number of plots
num_plots = len(data_dic)
# Create a figure with 2 rows and 4 columns
fig, axes = plt.subplots(2, 4, figsize=(20, 10), constrained_layout=True)
axes = axes.flatten() # Flatten the axes for easier indexing
# Define the color mapping
color_map = {
"Critical": "darkred",
"High": "red",
"Medium": "orange",
"Low": "green"
}
# Create a single legend for the entire figure
handles = [plt.Line2D([0], [0], marker='o', color='w', label=level,
markersize=10, markerfacecolor=color) for level, color in color_map.items()]
fig.legend(handles, color_map.keys(), loc='upper right', fontsize=12, title="Threat Level")
# Iterate over the data dictionary and create each subplot
for i, (title, data) in enumerate(data_dic.items()):
if i >= len(axes): # Break if more plots than subplots
break
ax = axes[i]
# Prepare data for the pie chart
labels = data.index
values = data.values
colors = [color_map[label] for label in labels]
total = values.sum() # Total sum of values
# Create a donut plot
wedges, texts, autotexts = ax.pie(
values,
labels=[f"{label}\n{value} ({value/total:.0%})" for label, value in zip(labels, values)],
autopct='',
startangle=90,
colors=colors,
wedgeprops=dict(width=0.4)
)
# Add the total sum at the center of the donut
ax.text(0, 0, str(total), ha='center', va='center', fontsize=14, fontweight='bold')
# Set title
ax.set_title(title, fontsize=14)
# Remove extra subplots if fewer data points
for i in range(num_plots, len(axes)):
fig.delaxes(axes[i])
# Display the plots
plt.show()
#---------------------------------------------Generate Executive Summary------------------------------------------------
# Generate executive Summary
class ExecutiveReport(FPDF):
def header(self):
self.set_font('Arial', 'B', 12)
self.cell(0, 10, 'Executive Report: Cybersecurity Incident Analysis', align='C', ln=True)
self.ln(10)
def footer(self):
self.set_y(-15)
self.set_font('Arial', 'I', 8)
self.cell(0, 10, f'Page {self.page_no()}', align='C')
def section_title(self, title):
self.set_font('Arial', 'B', 12)
self.cell(0, 10, title, ln=True)
self.ln(5)
def section_body(self, body):
self.set_font('Arial', '', 11)
self.multi_cell(0, 10, body)
self.ln()
def add_table(self, headers, data, col_widths):
self.set_font('Arial', 'B', 10)
for i, header in enumerate(headers):
self.cell(col_widths[i], 10, header, border=1, align='C')
self.ln()
self.set_font('Arial', '', 10)
for row in data:
for i, item in enumerate(row):
self.cell(col_widths[i], 10, str(item), border=1, align='C')
self.ln()
# Extract attacks key metrics for the report
def extract_attacks_key_metrics(df):
critical_issues_df = df[df["Severity"] == "Critical"]
resolved_issues_df = df[df["Status"].isin(["Resolved", "Closed"])]
attack_types = ["Phishing", "Malware", "DDOS", "Data Leak"]
phishing_attack_departement_affected = df[df["Login Attempts"] > 10]
malware_attack_departement_affected = df[df["Num Files Accessed"] > 50]
ddos_attack_departement_affected = df[df["Session Duration in Second"] > 3600]
ddos_attack_departement_affected = df[df["Data Transfer MB"] > 500]
data_leak_attack_departement_affected = df[df["Data Transfer MB"] > 500]
attack_type_departement_affected_dic = {
"Phishing": phishing_attack_departement_affected,
"Malware": malware_attack_departement_affected,
"DDOS": ddos_attack_departement_affected,
"Data Leak": data_leak_attack_departement_affected
}
metrics_dic = {
"Total Issues": len(df),
"Critical Issues": len(critical_issues_df),
"Resolved Issues": len(resolved_issues_df),
"Unresolved Issues": len(df) - len(resolved_issues_df),
"Phishing Attacks": len(df[df["Login Attempts"] > 10]),
"Malware Attacks": len(df[df["Num Files Accessed"] > 50]),
"DDOS Attacks": len(df[df["Session Duration in Second"] > 3600]),
"Data Leak Attacks": len(df[df["Data Transfer MB"] > 500]),
}
attack_metrics_df = pd.DataFrame(metrics_dic, index=["Value"]).T
Incident_summary_dic = {
"Total Issues": metrics_dic["Total Issues"],
"Critical Issues": metrics_dic["Critical Issues"],
"Resolved Issues": metrics_dic["Resolved Issues"],
"Unresolved Issues": metrics_dic["Unresolved Issues"]}
Insident_summary_df = pd.DataFrame(Incident_summary_dic, index=["Value"]).T
attack_scenarios_dic = {
"Phishing Attacks": metrics_dic['Phishing Attacks'],
"Malware Attacks": metrics_dic['Malware Attacks'],
"DDOS Attacks": metrics_dic['DDOS Attacks'],
"Data Leak Attacks": metrics_dic['Data Leak Attacks']}
attack_scenarios_df = pd.DataFrame(attack_scenarios_dic, index=["Value"]).T
critical_issues_sample_df = critical_issues_df.head(10)[["Issue ID", "Category",
"Threat Level", "Severity",
"Status", "Risk Level", "Impact Score",
"Issue Response Time Days", "Department Affected",
"Cost", "Defense Action"]]
return metrics_dic, Incident_summary_dic, attack_scenarios_dic, attack_type_departement_affected_dic, critical_issues_df, critical_issues_sample_df
#-------------------------------plot incident_summary and attack_scenario----------------------------------
def millions_formatter(x, pos):
return f"{x / 1e6:.1f}"
def plot_attacks_metrics(incident_summary_dic, attack_scenarios_dic, attack_type_departement_affected_dic):
# Convert dictionaries to dataframes
incident_summary_df = pd.DataFrame(incident_summary_dic, index=["Value"]).T
attack_scenarios_df = pd.DataFrame(attack_scenarios_dic, index=["Value"]).T
# Extract the attack dataframes
phishing_df = attack_type_departement_affected_dic["Phishing"]
malware_df = attack_type_departement_affected_dic["Malware"]
ddos_df = attack_type_departement_affected_dic["DDOS"]
data_leak_df = attack_type_departement_affected_dic["Data Leak"]
# List of all data to plot
plot_data = [
(incident_summary_df, "Incident Summary", "index", "Value"),
(attack_scenarios_df, "Attack Scenarios", "index", "Value"),
(phishing_df, "Phishing Attack - Dept vs Cost", "Department Affected", "Cost"),
(malware_df, "Malware Attack - Dept vs Cost", "Department Affected", "Cost"),
(ddos_df, "DDOS Attack - Dept vs Cost", "Department Affected", "Cost"),
(data_leak_df, "Data Leak Attack - Dept vs Cost", "Department Affected", "Cost")
]
# Define a color palette for the subplots
colors = ['steelblue', 'darkorange', 'seagreen', 'crimson', 'gold', 'purple']
# Create subplots
fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(18, 10))
axes = axes.flatten() # Flatten the axes array for easy iteration
for i, (df, title, x_col, y_col) in enumerate(plot_data):
ax = axes[i]
# Assign a unique color to each plot
color = colors[i]
if not df.empty: # Ensure dataframe is not empty
if x_col == "index": # Handle incident_summary_df and attack_scenarios_df
df_sorted = df.sort_values(by=y_col, ascending=False)
ax.barh(df_sorted.index, df_sorted[y_col], color=color, edgecolor='none')
ax.set_title(title, fontsize=12)
ax.set_xlabel(y_col)
ax.set_ylabel(x_col)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
else: # Handle attack-type dataframes
df_sorted = df.sort_values(by=y_col, ascending=False)
ax.barh(df_sorted[x_col], df_sorted[y_col], color=color, edgecolor='none')
# Format x-axis values as "M $"
ax.xaxis.set_major_formatter(FuncFormatter(millions_formatter))
ax.set_title(title, fontsize=12)
ax.set_xlabel(y_col if y_col != "Cost" else "Cost (in M $)")
ax.set_ylabel(x_col)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
else:
# Handle empty dataframes
ax.text(0.5, 0.5, "No Data Available", horizontalalignment='center', verticalalignment='center', fontsize=12)
ax.set_title(title, fontsize=12)
ax.set_xticks([])
ax.set_yticks([])
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
# Hide any unused axes if fewer than 6 plots
for j in range(len(plot_data), len(axes)):
axes[j].axis("off")
# Adjust layout and display
plt.tight_layout()
plt.show()
# Generate the PDF report
def generate_attacks_pdf_report(metrics, Insident_summary, attack_scenarios, critical_issues_df):
report = ExecutiveReport()
report.add_page()
report.section_title("Incident Summary")
summary_body = (
f"Total Issues: {metrics['Total Issues']}\n"
f"Critical Issues: {metrics['Critical Issues']}\n"
f"Resolved Issues: {metrics['Resolved Issues']}\n"
f"Unresolved Issues: {metrics['Unresolved Issues']}\n"
)
report.section_body(summary_body)
report.section_title("Attack Scenarios")
attack_body = (
f"Phishing Attacks: {metrics['Phishing Attacks']}\n"
f"Malware Attacks: {metrics['Malware Attacks']}\n"
f"DDOS Attacks: {metrics['DDOS Attacks']}\n"
f"Data Leak Attacks: {metrics['Data Leak Attacks']}\n"
)
report.section_body(attack_body)
report.section_title("Critical Issues Overview")
critical_issues_sample_df = critical_issues_df.head(10)[["Issue ID", "Category", "Threat Level", "Severity", "Status", "Risk Level",
"Impact Score", "Issue Response Time Days", "Department Affected", "Cost", "Defense Action"]]
headers = critical_issues_sample_df.columns.tolist()
data = critical_issues_sample_df.values.tolist()
col_widths = [30, 40, 30, 30, 30, 30, 30, 30, 100, 30, 100]
report.add_table(headers, data, col_widths)
# Save the report
report.output(Executive_Cybersecurity_Attack_Report_on_google_drive)
print(f"Executive Report saved to {Executive_Cybersecurity_Attack_Report_on_google_drive}")
#------------Metric extraction pipiline------------
def attacks_key_metrics_pipeline(df):
metrics_dic, incident_summary_dic, attack_scenarios_dic, attack_type_departement_affected_dic, \
critical_issues_df, critical_issues_sample_df = extract_attacks_key_metrics(df)
print("\n")
plot_attacks_metrics(incident_summary_dic, attack_scenarios_dic, attack_type_departement_affected_dic)
print("\n")
print("\nCritical Issues Sample\n")
display(critical_issues_sample_df)
return metrics_dic, incident_summary_dic, attack_scenarios_dic, critical_issues_df
def plot_executive_report_metrics(data_dic):
plot_executive_report_bars(data_dic)
print("\n")
print("\n")
plot_executive_report_donut_charts(data_dic)
#-------------------------------------------Main Pipeline----------------------------------------------------------------------------
def main_executive_report_pipeline(df):
report_summary_data_dic = generate_executive_report(df)
plot_executive_report_metrics(report_summary_data_dic)
def main_attacks_executive_summary_reporting_pipeline(df):
metrics, incident_summary, attack_scenarios, critical_issues_df = attacks_key_metrics_pipeline(df)
generate_attacks_pdf_report(metrics, incident_summary, attack_scenarios, critical_issues_df)
#-----------------------------------------Main Dashboard-----------------------------------------------------------------------------
def main_dashboard():
simulated_attacks_file_path = "/content/drive/My Drive/Cybersecurity Data/simulated_attacks_df.csv"
#load attacks data from drive
attack_simulation_df = pd.read_csv(simulated_attacks_file_path)
print("\nDashboar main_attacks_executive_summary_reporting_pipeline\n")
main_executive_report_pipeline(attack_simulation_df)
print("\nDashboar attacks_executive_summary_reporting_pipeline\n")
main_attacks_executive_summary_reporting_pipeline(attack_simulation_df)
if __name__ == "__main__":
main_dashboard()
Dashboar main_attacks_executive_summary_reporting_pipeline report_summary_df
| Total Attack | Attack Volume Severity | Impact in Cost(M$) | Resolved Issues | Outstanding Issues | Outstanding Issues Avg Response Time | Solved Issues Avg Response Time | |
|---|---|---|---|---|---|---|---|
| Critical | 1332 | 402 | 650.0 | 677 | 655 | 485.0 | 6.0 |
| High | 114 | 416 | 683.0 | 61 | 53 | 446.0 | 5.0 |
| Low | 46 | 415 | 543.0 | 28 | 18 | 435.0 | 4.0 |
| Medium | 108 | 367 | 484.0 | 50 | 58 | 518.0 | 5.0 |
average_response_time
| Average Response Time in days | Average Response Time in hours | Average Response Time in minutes | |
|---|---|---|---|
| 0 | 240 | 4416 | 264960 |
Top 5 issues impact with Addaptative Defense Mechanism
| Issue ID | Threat Level | Severity | Issue Response Time Days | Department Affected | Cost | Defense Action | |
|---|---|---|---|---|---|---|---|
| 1587 | ISSUE-0988 | Critical | Medium | 9.0 | Finance | 2287325.0 | Isolate Affected System & Restrict User Access... |
| 314 | ISSUE-0315 | Critical | Medium | 1.0 | Finance | 2391475.0 | Isolate Affected System & Restrict User Access... |
| 504 | ISSUE-0505 | Medium | Medium | 4.0 | Legal | 287805.0 | Routine Monitoring | Limit Data Transfer |
| 1377 | ISSUE-0778 | High | Medium | 6.0 | C-Suite Executives | 2262165.0 | Alert Security Team & Review Logs | Lock Accou... |
| 1173 | ISSUE-0574 | Critical | Low | 64.0 | HR | 2176402.5 | Increase Monitoring & Schedule Review | Lock A... |
Dashboar attacks_executive_summary_reporting_pipeline
Critical Issues Sample
| Issue ID | Category | Threat Level | Severity | Status | Risk Level | Impact Score | Issue Response Time Days | Department Affected | Cost | Defense Action | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 8 | ISSUE-0009 | Phishing Attack | Critical | Critical | In Progress | Critical | 62.69 | 704.0 | Finance | 2122814.0 | Immediate System-wide Shutdown & Investigation... |
| 9 | ISSUE-0010 | Phishing Attack | Critical | Critical | Open | Critical | 72.44 | 810.0 | Legal | 1255844.0 | Immediate System-wide Shutdown & Investigation... |
| 10 | ISSUE-0011 | Control Effectiveness | Critical | Critical | Open | Critical | 41.04 | 870.0 | Sales | 1931150.0 | Immediate System-wide Shutdown & Investigation... |
| 17 | ISSUE-0018 | Risk Exposure | Medium | Critical | Closed | Low | 2.00 | 1.0 | IT | 1478822.0 | Increase Monitoring & Investigate | Limit Data... |
| 18 | ISSUE-0019 | Asset Inventory Accuracy | Critical | Critical | Open | Critical | 78.27 | 773.0 | IT | 2184356.0 | Immediate System-wide Shutdown & Investigation... |
| 19 | ISSUE-0020 | Data Leak | Critical | Critical | Open | Critical | 53.29 | 507.0 | Finance | 1788848.0 | Immediate System-wide Shutdown & Investigation... |
| 20 | ISSUE-0021 | Asset Inventory Accuracy | Critical | Critical | In Progress | Critical | 61.31 | 428.0 | External Contractors | 2318963.0 | Immediate System-wide Shutdown & Investigation... |
| 24 | ISSUE-0025 | Malware | Critical | Critical | Closed | Critical | 52.01 | 10.0 | Sales | 410114.0 | Immediate System-wide Shutdown & Investigation... |
| 28 | ISSUE-0029 | Legal Compliance | Medium | Critical | Open | High | 9.49 | 303.0 | Legal | 792650.0 | Increase Monitoring & Investigate | Limit Data... |
| 32 | ISSUE-0033 | DDOS | Critical | Critical | Closed | Critical | 64.04 | 7.0 | Sales | 1139792.0 | Immediate System-wide Shutdown & Investigation... |
Executive Report saved to /content/drive/My Drive/Cybersecurity Data/Executive_Cybersecurity_Attack_Report.pdf
Attack symulation version2¶
In [ ]:
from datetime import datetime
import numpy as np
import pandas as pd
import random
import socket
import struct
# -------------------- Attack Classes --------------------
class BaseAttack:
def __init__(self, df):
self.df = df.copy()
self.ip_generator = IPAddressGenerator()
def apply(self):
raise NotImplementedError("Each attack must implement the apply() method.")
class PhishingAttack(BaseAttack):
def apply(self):
targets = self.df[self.df["Category"] == "Access Control"].sample(frac=0.1, random_state=42)
anomaly_magnitude = 1.0
self.df.loc[targets.index, "Login Attempts"] += anomaly_magnitude * np.random.poisson(lam=self.df["Login Attempts"].mean(), size=len(targets))
self.df.loc[targets.index, "Impact Score"] += anomaly_magnitude * np.random.normal(loc=self.df["Impact Score"].mean(), scale=self.df["Impact Score"].std(), size=len(targets)).astype(int)
self.df.loc[targets.index, "Threat Score"] += anomaly_magnitude * np.random.normal(loc=self.df["Threat Score"].mean(), scale=self.df["Threat Score"].std(), size=len(targets)).astype(int)
self.df.loc[targets.index, "Attack Type"] = "Phishing"
return self.df
class MalwareAttack(BaseAttack):
def apply(self):
targets = self.df[self.df["Category"] == "System Vulnerability"].sample(frac=0.1, random_state=42)
anomaly_magnitude = 1.0
self.df.loc[targets.index, "Num Files Accessed"] += anomaly_magnitude * np.random.poisson(lam=self.df["Num Files Accessed"].mean(), size=len(targets))
self.df.loc[targets.index, "Impact Score"] += anomaly_magnitude * np.random.normal(loc=self.df["Impact Score"].mean(), scale=self.df["Impact Score"].std(), size=len(targets)).astype(int)
self.df.loc[targets.index, "Threat Score"] += anomaly_magnitude * np.random.normal(loc=self.df["Threat Score"].mean(), scale=self.df["Threat Score"].std(), size=len(targets)).astype(int)
self.df.loc[targets.index, "Attack Type"] = "Malware"
return self.df
class DDoSAttack(BaseAttack):
def apply(self):
targets = self.df[self.df["Category"] == "Network Security"].sample(frac=0.2, random_state=42)
anomaly_magnitude = 1.0
self.df.loc[targets.index, "Session Duration in Second"] += anomaly_magnitude * np.random.exponential(scale=self.df["Session Duration in Second"].mean(), size=len(targets)).astype(int)
self.df.loc[targets.index, "Impact Score"] += anomaly_magnitude * np.random.exponential(scale=self.df["Impact Score"].mean(), size=len(targets)).astype(int)
self.df.loc[targets.index, "Threat Score"] += anomaly_magnitude * np.random.exponential(scale=self.df["Threat Score"].mean(), size=len(targets)).astype(int)
self.df.loc[targets.index, "Login Attempts"] += anomaly_magnitude * np.random.poisson(lam=self.df["Login Attempts"].mean(), size=len(targets))
self.df.loc[targets.index, "Source IP Address"] = "192.168.1.10"
self.df.loc[targets.index, "Destination IP Address"] = "192.168.1.10"
self.df.loc[targets.index, "Attack Type"] = "DDoS"
return self.df
class DataLeakAttack(BaseAttack):
def apply(self):
targets = self.df[self.df["Category"] == "Data Breach"].sample(frac=0.1, random_state=42)
anomaly_magnitude = 1.0
transfer_log_mean = np.log(self.df["Data Transfer MB"].mean())
transfer_log_std = np.log(self.df["Data Transfer MB"].std())
self.df.loc[targets.index, "Data Transfer MB"] += anomaly_magnitude * np.random.lognormal(mean=transfer_log_mean, sigma=transfer_log_std, size=len(targets))
self.df.loc[targets.index, "Impact Score"] += anomaly_magnitude * np.random.lognormal(mean=np.log(self.df["Impact Score"].mean()), sigma=transfer_log_std, size=len(targets))
self.df.loc[targets.index, "Threat Score"] += anomaly_magnitude * np.random.lognormal(mean=np.log(self.df["Threat Score"].mean()), sigma=transfer_log_std, size=len(targets))
self.df.loc[targets.index, "Attack Type"] = "Data Leak"
return self.df
class InsiderThreatAttack(BaseAttack):
def apply(self):
self.df['hour'] = pd.to_datetime(self.df['Timestamps'], errors='coerce').dt.hour
late_hours = self.df[(self.df['hour'] < 6) | (self.df['hour'] > 23)]
targets = late_hours.sample(frac=0.1, random_state=42)
anomaly_magnitude = 1.0
transfer_log_mean = np.log(self.df["Data Transfer MB"].mean())
transfer_log_std = np.log(self.df["Data Transfer MB"].std())
self.df.loc[targets.index, "Access Restricted Files"] = True
self.df.loc[targets.index, "Data Transfer MB"] += anomaly_magnitude * np.random.lognormal(mean=transfer_log_mean, sigma=transfer_log_std, size=len(targets))
self.df.loc[targets.index, "Impact Score"] += anomaly_magnitude * np.random.normal(loc=self.df["Impact Score"].mean(), scale=self.df["Impact Score"].std(), size=len(targets)).astype(int)
self.df.loc[targets.index, "Threat Score"] += anomaly_magnitude * np.random.normal(loc=self.df["Threat Score"].mean(), scale=self.df["Threat Score"].std(), size=len(targets)).astype(int)
self.df.loc[targets.index, "Attack Type"] = "Insider Threat"
return self.df
class RansomwareAttack(BaseAttack):
def apply(self):
targets = self.df[self.df["Category"] == "System Vulnerability"].sample(frac=0.02, random_state=42)
anomaly_magnitude = 1.0
self.df.loc[targets.index, "CPU Usage %"] += anomaly_magnitude * np.random.normal(loc=self.df["CPU Usage %"].mean(), scale=self.df["CPU Usage %"].std(), size=len(targets))
self.df.loc[targets.index, "Memory Usage MB"] += anomaly_magnitude * np.random.lognormal(mean=np.log(self.df["Memory Usage MB"].mean()), sigma=np.log(self.df["Memory Usage MB"].std()), size=len(targets))
self.df.loc[targets.index, "Num Files Accessed"] += anomaly_magnitude * np.random.poisson(lam=self.df["Num Files Accessed"].mean(), size=len(targets))
self.df.loc[targets.index, "Threat Score"] += anomaly_magnitude * np.random.normal(loc=self.df["Threat Score"].mean(), scale=self.df["Threat Score"].std(), size=len(targets)).astype(int)
self.df.loc[targets.index, "Impact Score"] += anomaly_magnitude * np.random.normal(loc=self.df["Impact Score"].mean(), scale=self.df["Impact Score"].std(), size=len(targets)).astype(int)
self.df.loc[targets.index, "Attack Type"] = "Ransomware"
return self.df
class EarlyAnomalyDetectorClass:
#def __init__(self):
# pass
def __init__(self, df):
self.df = df.copy()
def detect_early_anomalies(self, column='Threat Score'):
Q1 = self.df[column].quantile(0.25)
Q3 = self.df[column].quantile(0.75)
IQR = Q3 - Q1
self.df['Actual Anomaly'] = ((self.df[column] < Q1 - 1.5 * IQR) | (self.df[column] > Q3 + 1.5 * IQR)).astype(int)
#get anomlous dataframe
df_anomalies = self.df[self.df['Actual Anomaly'] == 1]
#get normal dataframe
df_normal = self.df[self.df['Actual Anomaly'] == 0]
return df_anomalies, df_normal
class DataCombiner:
def __init__(self, normal_df, anomalous_df):
self.normal_df = normal_df.copy()
self.anomalous_df = anomalous_df.copy()
def combine_data(self):
combined_df = pd.concat([self.normal_df, self.anomalous_df], ignore_index=True)
return combined_df
class IPAddressGenerator:
"""A class for generating random IPv4 addresses and pairs."""
def __init__(self):
pass
def generate_random_ip(self):
"""Generates a random IPv4 address."""
return socket.inet_ntoa(struct.pack('>I', random.randint(1, 0xffffffff)))
def generate_ip_pair(self):
"""Generates a random source and destination IPv4 address pair."""
source_ip = self.generate_random_ip()
destination_ip = self.generate_random_ip()
return source_ip, destination_ip
# -------------------- Combined Runner --------------------
def run_selected_attacks(df, selected_attacks, verbose=True):
attack_map = {
"phishing": PhishingAttack,
"malware": MalwareAttack,
"ddos": DDoSAttack,
"data_leak": DataLeakAttack,
"insider": InsiderThreatAttack,
"ransomware": RansomwareAttack
}
if df is None:
raise ValueError("Input DataFrame is None at the start of attack simulation.")
for attack in selected_attacks:
if verbose: print(f"[+] Applying {attack.capitalize()} Attack")
attack_class = attack_map[attack]
df = attack_class(df).apply()
if df is None:
raise ValueError(f"Attack {attack} returned None. Ensure its `.apply()` method returns a DataFrame.")
return df
#------------------------------Main attacks simulation pipeline----------------------------
def main_attacks_simulation_pipeline():
#data sets paths
anomalous_flaged_production_df = "/content/drive/My Drive/Cybersecurity Data/normal_and_anomalous_flaged_df.csv"
file_production_data_folder = "/content/drive/My Drive/Cybersecurity Data/"
selected_attacks=["phishing", "malware", "ddos", "data_leak", "insider", "ransomware"]
# Load the dataset
production_df = pd.read_csv(anomalous_flaged_production_df)
production_df.head()
#detect production data early anomalous
# Check if production_df is loaded correctly
if production_df is not None:
df_anomalies, df_normal = EarlyAnomalyDetectorClass(production_df).detect_early_anomalies()
else:
print("Error: production_df is None. Please check the file path.")
return # Exit the function if data loading failed
#df_anomalies_copy = df_anomalies.copy() # Create a copy here
#display(df_anomalies_copy.head())
#df = DataCombiner(df_normal, df_anomalies_copy).combine_data()
#simulate the attacks on anomalous data frame
simulated_attacks_df = run_selected_attacks(df_anomalies, selected_attacks, verbose=True)
#df.head()
#Combined normal and anomalous data frames
combined_normal_and_simulated_attacks_df = DataCombiner(df_normal, simulated_attacks_df).combine_data()
#combined_normal_and_simulated_attacks_df.head()
#save the combined data frame to google drive
save_dataframe_to_drive(combined_normal_and_simulated_attacks_df,
file_production_data_folder+"combined_normal_and_simulated_attacks_class_df.csv")
display(combined_normal_and_simulated_attacks_df.head())
if __name__ == "__main__":
main_attacks_simulation_pipeline()
[+] Applying Phishing Attack [+] Applying Malware Attack [+] Applying Ddos Attack [+] Applying Data_leak Attack [+] Applying Insider Attack [+] Applying Ransomware Attack DataFrame saved to: /content/drive/My Drive/Cybersecurity Data/combined_normal_and_simulated_attacks_class_df.csv
| Issue ID | Issue Key | Issue Name | Issue Volume | Category | Severity | Status | Reporters | Assignees | Date Reported | ... | Color | Pred Threat | anomaly_score | is_anomaly | Actual Anomaly | Attack Type | Source IP Address | Destination IP Address | hour | Access Restricted Files | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ISSUE-0001 | KEY-0001 | Unauthorized Access Leading to Data Exposure | 1 | Data Breach | Low | Closed | Reporter 7 | Assignee 16 | 2023-12-07 | ... | Orange | 0 | 0 | False | 0 | NaN | NaN | NaN | NaN | NaN |
| 1 | ISSUE-0002 | KEY-0002 | Increased Exposure due to Insufficient Data En... | 1 | Risk Exposure | Low | In Progress | Reporter 1 | Assignee 4 | 2023-05-05 | ... | Orange | 0 | 0 | False | 0 | NaN | NaN | NaN | NaN | NaN |
| 2 | ISSUE-0003 | KEY-0003 | Non-Compliance with Data Protection Regulations | 1 | Legal Compliance | Medium | Closed | Reporter 3 | Assignee 6 | 2024-05-03 | ... | Orange-Red | 0 | 0 | False | 0 | NaN | NaN | NaN | NaN | NaN |
| 3 | ISSUE-0004 | KEY-0004 | Insufficient Coverage in Annual Risk Assessment | 1 | Risk Assessment Coverage | Low | Resolved | Reporter 3 | Assignee 17 | 2025-06-22 | ... | Orange | 0 | 0 | False | 0 | NaN | NaN | NaN | NaN | NaN |
| 4 | ISSUE-0005 | KEY-0005 | Inconsistent Review of Security Policies | 1 | Management Oversight | High | In Progress | Reporter 7 | Assignee 13 | 2024-03-28 | ... | Red | 0 | 0 | False | 0 | NaN | NaN | NaN | NaN | NaN |
5 rows × 42 columns
Execurive Dashboard¶
In [ ]:
def generate_executive_report(df):
# Threat statistics
total_theats = df.groupby("Threat Level").size()
severity_stats = df.groupby("Severity").size()
impact_cost_stats = round(df.groupby("Severity")["Cost"].sum()/ 1_000_000)
resolved_stats = df[df["Status"].isin(["Resolved", "Closed"])].groupby("Threat Level").size()
out_standing_issues = df[df["Status"].isin(["Open", "In Progress"])].groupby("Threat Level").size()
outstanding_issues_avg_resp_time = round(df[df["Status"].isin(["Open", "In Progress"])].groupby("Threat Level")["Issue Response Time Days"].mean())
solved_issues_avg_resp_time = round(df[df["Status"].isin(["Resolved", "Closed"])].groupby("Threat Level")["Issue Response Time Days"].mean())
# Top 5 issues
top_issues = df.nlargest(5, "Threat Score")
# Average response time
overall_avg_response_time = df["Issue Response Time Days"].mean()
report_summary_data_dic = {
"Total Attack": total_theats,
"Attack Volume Severity": severity_stats,
"Impact in Cost(M$)": impact_cost_stats,
"Resolved Issues": resolved_stats,
"Outstanding Issues": out_standing_issues,
"Outstanding Issues Avg Response Time": outstanding_issues_avg_resp_time,
"Solved Issues Avg Response Time": solved_issues_avg_resp_time,
"Top 5 Issues": top_issues.to_dict(),
"Overall Average Response Time(days)": overall_avg_response_time
}
top_five_issues_df = pd.DataFrame(report_summary_data_dic.pop("Top 5 Issues"))
top_five_issues_df["cost"] = top_five_issues_df["Cost"].apply(lambda x: round(x/1_000_000))
average_response_time = round(report_summary_data_dic.pop("Overall Average Response Time(days)"))
# Convert numeric columns to numeric type before creating the DataFrame
for col in ["Impact in Cost(M$)", "Outstanding Issues Avg Response Time", "Solved Issues Avg Response Time"]:
report_summary_data_dic[col] = pd.to_numeric(report_summary_data_dic[col], errors='coerce')
# Create report_summary_df from report_summary_data_dic
report_summary_df = pd.DataFrame(report_summary_data_dic)
# Apply round to numeric columns only after creating the DataFrame
report_summary_df = report_summary_df.apply(lambda x: round(x) if x.dtype.kind in 'biufc' else x)
top_five_incidents_defense_df = top_five_issues_df[["Issue ID", "Threat Level", "Severity",
"Issue Response Time Days", "Department Affected", "Cost", "Defense Action"]]
days = 184
hours = days * 24
minutes = days * 1440
average_response_time ={
"Average Response Time in days" : average_response_time,
"Average Response Time in hours" : hours,
"Average Response Time in minutes" : minutes
}
average_response_time_df = pd.DataFrame(average_response_time, index=[0])
print("\nreport_summary_df\n")
display(report_summary_df)
print("\naverage_response_time\n")
display(average_response_time_df)
print("\nTop 5 issues impact with Addaptative Defense Mechanism\n")
display(top_five_incidents_defense_df)
return report_summary_data_dic
#-------------------------------------------- Plot Executive Report metrics---------------------------------------------------------------
#Bar chart--
def plot_executive_report_bars(data_dic):
# Define the number of plots
num_plots = len(data_dic)
# Create a figure with 2 rows and 4 columns
fig, axes = plt.subplots(2, 4, figsize=(20, 10), constrained_layout=True)
axes = axes.flatten() # Flatten the axes for easier indexing
# Define the colors for each plot
colors = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b", "#e377c2"]
# Iterate over the data dictionary and create each subplot
for i, (title, data) in enumerate(data_dic.items()):
if i >= len(axes): # Break if more plots than subplots
break
ax = axes[i]
# Sort data for ascending bars
sorted_data = data.sort_values()
# Plot the horizontal bar chart
ax.barh(sorted_data.index, sorted_data.values, color=colors[i % len(colors)])
# Customize the subplot
ax.set_title(title, fontsize=14)
ax.set_facecolor("#f5f5f5") # Light gray background
ax.spines['top'].set_visible(False) # Remove top border
ax.spines['right'].set_visible(False) # Remove right border
ax.spines['left'].set_visible(False) # Remove left border
ax.spines['bottom'].set_visible(False) # Remove bottom border
ax.xaxis.set_visible(False) # Hide the x-axis
for j, v in enumerate(sorted_data.values):
ax.text(v, j, str(v), va='center', fontsize=10) # Add labels
# Remove extra subplots if fewer data points
for i in range(num_plots, len(axes)):
fig.delaxes(axes[i])
# Display the plots
plt.show()
#donut chart---------------------
def plot_executive_report_donut_charts(data_dic):
# Define the number of plots
num_plots = len(data_dic)
# Create a figure with 2 rows and 4 columns
fig, axes = plt.subplots(2, 4, figsize=(20, 10), constrained_layout=True)
axes = axes.flatten() # Flatten the axes for easier indexing
# Define the color mapping
color_map = {
"Critical": "darkred",
"High": "red",
"Medium": "orange",
"Low": "green"
}
# Create a single legend for the entire figure
handles = [plt.Line2D([0], [0], marker='o', color='w', label=level,
markersize=10, markerfacecolor=color) for level, color in color_map.items()]
fig.legend(handles, color_map.keys(), loc='upper right', fontsize=12, title="Threat Level")
# Iterate over the data dictionary and create each subplot
for i, (title, data) in enumerate(data_dic.items()):
if i >= len(axes): # Break if more plots than subplots
break
ax = axes[i]
# Prepare data for the pie chart
labels = data.index
values = data.values
colors = [color_map[label] for label in labels]
total = values.sum() # Total sum of values
# Create a donut plot
wedges, texts, autotexts = ax.pie(
values,
labels=[f"{label}\n{value} ({value/total:.0%})" for label, value in zip(labels, values)],
autopct='',
startangle=90,
colors=colors,
wedgeprops=dict(width=0.4)
)
# Add the total sum at the center of the donut
ax.text(0, 0, str(total), ha='center', va='center', fontsize=14, fontweight='bold')
# Set title
ax.set_title(title, fontsize=14)
# Remove extra subplots if fewer data points
for i in range(num_plots, len(axes)):
fig.delaxes(axes[i])
# Display the plots
plt.show()
#---------------------------------------------Generate Executive Summary------------------------------------------------
# Generate executive Summary
class ExecutiveReport(FPDF):
def header(self):
self.set_font('Arial', 'B', 12)
self.cell(0, 10, 'Executive Report: Cybersecurity Incident Analysis', align='C', ln=True)
self.ln(10)
def footer(self):
self.set_y(-15)
self.set_font('Arial', 'I', 8)
self.cell(0, 10, f'Page {self.page_no()}', align='C')
def section_title(self, title):
self.set_font('Arial', 'B', 12)
self.cell(0, 10, title, ln=True)
self.ln(5)
def section_body(self, body):
self.set_font('Arial', '', 11)
self.multi_cell(0, 10, body)
self.ln()
def add_table(self, headers, data, col_widths):
self.set_font('Arial', 'B', 10)
for i, header in enumerate(headers):
self.cell(col_widths[i], 10, header, border=1, align='C')
self.ln()
self.set_font('Arial', '', 10)
for row in data:
for i, item in enumerate(row):
self.cell(col_widths[i], 10, str(item), border=1, align='C')
self.ln()
# Extract attacks key metrics for the report
def extract_attacks_key_metrics(df):
critical_issues_df = df[df["Severity"] == "Critical"]
resolved_issues_df = df[df["Status"].isin(["Resolved", "Closed"])]
attack_types = ["Phishing", "Malware", "DDOS", "Data Leak", "Insider Threats","Ransomware Attacks" ]
phishing_attack_departement_affected = df[df["Login Attempts"] > 10]
malware_attack_departement_affected = df[df["Num Files Accessed"] > 50]
ddos_attack_departement_affected = df[df["Session Duration in Second"] > 3600]
ddos_attack_departement_affected = df[df["Data Transfer MB"] > 500]
data_leak_attack_departement_affected = df[df["Data Transfer MB"] > 500]
insider_threat_attack_departement_affected = df[df["Access Restricted Files"] == True]
ransomware_attack_departement_affected = df[df["CPU Usage %"] > 70]
attack_type_departement_affected_dic = {
"Phishing": phishing_attack_departement_affected,
"Malware": malware_attack_departement_affected,
"DDOS": ddos_attack_departement_affected,
"Data Leak": data_leak_attack_departement_affected,
"Insider Threats": insider_threat_attack_departement_affected,
"Ransomware Attacks": ransomware_attack_departement_affected
}
metrics_dic = {
"Total Issues": len(df),
"Critical Issues": len(critical_issues_df),
"Resolved Issues": len(resolved_issues_df),
"Unresolved Issues": len(df) - len(resolved_issues_df),
"Phishing Attacks": len(df[df["Login Attempts"] > 10]),
"Malware Attacks": len(df[df["Num Files Accessed"] > 50]),
# Increased thresholds for DDoS attacks
"DDOS Attacks": len(df[(df["Session Duration in Second"] > 7200) & (df["Data Transfer MB"] > 1000)]), # Increased duration and data transfer
"Data Leak Attacks": len(df[df["Data Transfer MB"] > 500]),
"Insider Threats": len(df[df["Access Restricted Files"] == True]), # Assuming a column for insider threats
"Ransomware Attacks": len(df[df["CPU Usage %"] > 70]), # Example condition, adjust as needed
# New metrics for Insider Threats and Ransomware
"Insider Threats (Restricted Files)": len(df[(df["Access Restricted Files"] == True) & (df["Data Transfer MB"] > 100)]), # Example: Data exfiltration
"Insider Threats (Unusual Hours)": len(df[(df["Access Restricted Files"] == True) & ((df["hour"] < 6) | (df["hour"] > 23))]), #Example: Access during off-hours
"Ransomware Attacks (High CPU)": len(df[(df["CPU Usage %"] > 90)]), # High CPU usage
"Ransomware Attacks (File Encryption)": len(df[(df["CPU Usage %"] > 70) & (df["Num Files Accessed"] > 100)]) # File encryption activity
}
attack_metrics_df = pd.DataFrame(metrics_dic, index=["Value"]).T
Incident_summary_dic = {
"Total Issues": metrics_dic["Total Issues"],
"Critical Issues": metrics_dic["Critical Issues"],
"Resolved Issues": metrics_dic["Resolved Issues"],
"Unresolved Issues": metrics_dic["Unresolved Issues"]}
Insident_summary_df = pd.DataFrame(Incident_summary_dic, index=["Value"]).T
attack_scenarios_dic = {
"Phishing Attacks": metrics_dic['Phishing Attacks'],
"Malware Attacks": metrics_dic['Malware Attacks'],
"DDOS Attacks": metrics_dic['DDOS Attacks'],
"Data Leak Attacks": metrics_dic['Data Leak Attacks'],
"Insider Threats": metrics_dic['Insider Threats'],
"Ransomware Attacks": metrics_dic['Ransomware Attacks']}
attack_scenarios_df = pd.DataFrame(attack_scenarios_dic, index=["Value"]).T
critical_issues_sample_df = critical_issues_df.head(10)[["Issue ID", "Category",
"Threat Level", "Severity",
"Status", "Risk Level", "Impact Score",
"Issue Response Time Days", "Department Affected",
"Cost", "Defense Action"]]
return metrics_dic, Incident_summary_dic, attack_scenarios_dic, attack_type_departement_affected_dic, critical_issues_df, critical_issues_sample_df
#-------------------------------plot incident_summary and attack_scenario----------------------------------
def millions_formatter(x, pos):
return f"{x / 1e6:.1f}"
def plot_attacks_metrics(incident_summary_dic, attack_scenarios_dic, attack_type_departement_affected_dic):
# Convert dictionaries to dataframes
incident_summary_df = pd.DataFrame(incident_summary_dic, index=["Value"]).T
attack_scenarios_df = pd.DataFrame(attack_scenarios_dic, index=["Value"]).T
# Extract the attack dataframes
phishing_df = attack_type_departement_affected_dic["Phishing"]
malware_df = attack_type_departement_affected_dic["Malware"]
ddos_df = attack_type_departement_affected_dic["DDOS"]
data_leak_df = attack_type_departement_affected_dic["Data Leak"]
insider_threat_df = attack_type_departement_affected_dic["Insider Threats"]
ransomware_df = attack_type_departement_affected_dic["Ransomware Attacks"]
# List of all data to plot
plot_data = [
(incident_summary_df, "Incident Summary", "index", "Value"),
(attack_scenarios_df, "Attack Scenarios", "index", "Value"),
(phishing_df, "Phishing Attack - Dept vs Cost", "Department Affected", "Cost"),
(malware_df, "Malware Attack - Dept vs Cost", "Department Affected", "Cost"),
(ddos_df, "DDOS Attack - Dept vs Cost", "Department Affected", "Cost"),
(data_leak_df, "Data Leak Attack - Dept vs Cost", "Department Affected", "Cost"),
(insider_threat_df, "Insider Attack - Dept vs Cost", "Department Affected", "Cost"),
(ransomware_df, "Ransomware Attack - Dept vs Cost", "Department Affected", "Cost")
]
# Define a color palette for the subplots
colors = ['steelblue', 'darkorange', 'seagreen', 'crimson', 'gold', 'purple', 'teal', 'magenta']
# Create subplots
fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(18, 10))
axes = axes.flatten() # Flatten the axes array for easy iteration
for i, (df, title, x_col, y_col) in enumerate(plot_data):
ax = axes[i]
# Assign a unique color to each plot
color = colors[i]
if not df.empty: # Ensure dataframe is not empty
if x_col == "index": # Handle incident_summary_df and attack_scenarios_df
df_sorted = df.sort_values(by=y_col, ascending=False)
ax.barh(df_sorted.index, df_sorted[y_col], color=color, edgecolor='none')
ax.set_title(title, fontsize=12)
ax.set_xlabel(y_col)
ax.set_ylabel(x_col)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
else: # Handle attack-type dataframes
df_sorted = df.sort_values(by=y_col, ascending=False)
ax.barh(df_sorted[x_col], df_sorted[y_col], color=color, edgecolor='none')
# Format x-axis values as "M $"
ax.xaxis.set_major_formatter(FuncFormatter(millions_formatter))
ax.set_title(title, fontsize=12)
ax.set_xlabel(y_col if y_col != "Cost" else "Cost (in M $)")
ax.set_ylabel(x_col)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
else:
# Handle empty dataframes
ax.text(0.5, 0.5, "No Data Available", horizontalalignment='center', verticalalignment='center', fontsize=12)
ax.set_title(title, fontsize=12)
ax.set_xticks([])
ax.set_yticks([])
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
# Hide any unused axes if fewer than 6 plots
for j in range(len(plot_data), len(axes)):
axes[j].axis("off")
# Adjust layout and display
plt.tight_layout()
plt.show()
# Generate the PDF report
def generate_attacks_pdf_report(metrics, Insident_summary, attack_scenarios, critical_issues_df):
report = ExecutiveReport()
report.add_page()
report.section_title("Incident Summary")
summary_body = (
f"Total Issues: {metrics['Total Issues']}\n"
f"Critical Issues: {metrics['Critical Issues']}\n"
f"Resolved Issues: {metrics['Resolved Issues']}\n"
f"Unresolved Issues: {metrics['Unresolved Issues']}\n"
)
report.section_body(summary_body)
report.section_title("Attack Scenarios")
attack_body = (
f"Phishing Attacks: {metrics['Phishing Attacks']}\n"
f"Malware Attacks: {metrics['Malware Attacks']}\n"
f"DDOS Attacks: {metrics['DDOS Attacks']}\n"
f"Data Leak Attacks: {metrics['Data Leak Attacks']}\n"
f"Insider Threats: {metrics['Insider Threats']}\n" # Add insider threat data
f"Ransomware Attacks: {metrics['Ransomware Attacks']}\n" #Add ransomware data
)
report.section_body(attack_body)
report.section_title("Critical Issues Overview")
critical_issues_sample_df = critical_issues_df.head(10)[["Issue ID", "Category", "Threat Level", "Severity", "Status", "Risk Level",
"Impact Score", "Issue Response Time Days", "Department Affected", "Cost", "Defense Action"]]
headers = critical_issues_sample_df.columns.tolist()
data = critical_issues_sample_df.values.tolist()
col_widths = [30, 40, 30, 30, 30, 30, 30, 30, 100, 30, 100]
report.add_table(headers, data, col_widths)
# Save the report
report.output(Executive_Cybersecurity_Attack_Report_on_google_drive)
print(f"Executive Report saved to {Executive_Cybersecurity_Attack_Report_on_google_drive}")
#------------Metric extraction pipiline------------
def attacks_key_metrics_pipeline(df):
metrics_dic, incident_summary_dic, attack_scenarios_dic, attack_type_departement_affected_dic, \
critical_issues_df, critical_issues_sample_df = extract_attacks_key_metrics(df)
print("\n")
plot_attacks_metrics(incident_summary_dic, attack_scenarios_dic, attack_type_departement_affected_dic)
print("\n")
print("\nCritical Issues Sample\n")
display(critical_issues_sample_df)
return metrics_dic, incident_summary_dic, attack_scenarios_dic, critical_issues_df
def plot_executive_report_metrics(data_dic):
plot_executive_report_bars(data_dic)
print("\n")
print("\n")
plot_executive_report_donut_charts(data_dic)
#-------------------------------------------Main Pipeline----------------------------------------------------------------------------
def main_executive_report_pipeline(df):
report_summary_data_dic = generate_executive_report(df)
plot_executive_report_metrics(report_summary_data_dic)
def main_attacks_executive_summary_reporting_pipeline(df):
metrics, incident_summary, attack_scenarios, critical_issues_df = attacks_key_metrics_pipeline(df)
generate_attacks_pdf_report(metrics, incident_summary, attack_scenarios, critical_issues_df)
#-----------------------------------------Main Dashboard-----------------------------------------------------------------------------
def main_dashboard():
#simulated_attacks_file_path = "/content/drive/My Drive/Cybersecurity Data/simulated_attacks_df.csv"
simulated_attacks_file_path = "/content/drive/My Drive/Cybersecurity Data/combined_normal_and_simulated_attacks_class_df.csv"
#load attacks data from drive
attack_simulation_df = pd.read_csv(simulated_attacks_file_path)
print("\nDashboar main_attacks_executive_summary_reporting_pipeline\n")
main_executive_report_pipeline(attack_simulation_df)
print("\nDashboar attacks_executive_summary_reporting_pipeline\n")
main_attacks_executive_summary_reporting_pipeline(attack_simulation_df)
if __name__ == "__main__":
main_dashboard()
Dashboar main_attacks_executive_summary_reporting_pipeline report_summary_df
| Total Attack | Attack Volume Severity | Impact in Cost(M$) | Resolved Issues | Outstanding Issues | Outstanding Issues Avg Response Time | Solved Issues Avg Response Time | |
|---|---|---|---|---|---|---|---|
| Critical | 1332 | 402 | 650.0 | 677 | 655 | 485.0 | 6.0 |
| High | 114 | 416 | 683.0 | 61 | 53 | 446.0 | 5.0 |
| Low | 46 | 415 | 543.0 | 28 | 18 | 435.0 | 4.0 |
| Medium | 108 | 367 | 484.0 | 50 | 58 | 518.0 | 5.0 |
average_response_time
| Average Response Time in days | Average Response Time in hours | Average Response Time in minutes | |
|---|---|---|---|
| 0 | 240 | 4416 | 264960 |
Top 5 issues impact with Addaptative Defense Mechanism
| Issue ID | Threat Level | Severity | Issue Response Time Days | Department Affected | Cost | Defense Action | |
|---|---|---|---|---|---|---|---|
| 1591 | ISSUE-0726 | Critical | Critical | 797.0 | External Contractors | 2018480.0 | Immediate System-wide Shutdown & Investigation... |
| 1587 | ISSUE-0204 | Critical | Critical | 584.0 | HR | 2014148.0 | Immediate System-wide Shutdown & Investigation... |
| 1590 | ISSUE-0549 | Critical | Critical | 7.0 | Finance | 2284184.0 | Immediate System-wide Shutdown & Investigation... |
| 1588 | ISSUE-0488 | Critical | Critical | 7.0 | C-Suite Executives | 2155973.0 | Immediate System-wide Shutdown & Investigation... |
| 1595 | ISSUE-0512 | Critical | High | 393.0 | Legal | 2942903.0 | Escalate to Security Operations Center (SOC) &... |
Dashboar attacks_executive_summary_reporting_pipeline
Critical Issues Sample
| Issue ID | Category | Threat Level | Severity | Status | Risk Level | Impact Score | Issue Response Time Days | Department Affected | Cost | Defense Action | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 8 | ISSUE-0009 | Phishing Attack | Critical | Critical | In Progress | Critical | 62.69 | 704.0 | Finance | 2122814.0 | Immediate System-wide Shutdown & Investigation... |
| 9 | ISSUE-0010 | Phishing Attack | Critical | Critical | Open | Critical | 72.44 | 810.0 | Legal | 1255844.0 | Immediate System-wide Shutdown & Investigation... |
| 10 | ISSUE-0011 | Control Effectiveness | Critical | Critical | Open | Critical | 41.04 | 870.0 | Sales | 1931150.0 | Immediate System-wide Shutdown & Investigation... |
| 17 | ISSUE-0018 | Risk Exposure | Medium | Critical | Closed | Low | 2.00 | 1.0 | IT | 1478822.0 | Increase Monitoring & Investigate | Limit Data... |
| 18 | ISSUE-0019 | Asset Inventory Accuracy | Critical | Critical | Open | Critical | 78.27 | 773.0 | IT | 2184356.0 | Immediate System-wide Shutdown & Investigation... |
| 19 | ISSUE-0020 | Data Leak | Critical | Critical | Open | Critical | 53.29 | 507.0 | Finance | 1788848.0 | Immediate System-wide Shutdown & Investigation... |
| 20 | ISSUE-0021 | Asset Inventory Accuracy | Critical | Critical | In Progress | Critical | 61.31 | 428.0 | External Contractors | 2318963.0 | Immediate System-wide Shutdown & Investigation... |
| 24 | ISSUE-0025 | Malware | Critical | Critical | Closed | Critical | 52.01 | 10.0 | Sales | 410114.0 | Immediate System-wide Shutdown & Investigation... |
| 28 | ISSUE-0029 | Legal Compliance | Medium | Critical | Open | High | 9.49 | 303.0 | Legal | 792650.0 | Increase Monitoring & Investigate | Limit Data... |
| 32 | ISSUE-0033 | DDOS | Critical | Critical | Closed | Critical | 64.04 | 7.0 | Sales | 1139792.0 | Immediate System-wide Shutdown & Investigation... |
Executive Report saved to /content/drive/My Drive/Cybersecurity Data/Executive_Cybersecurity_Attack_Report.pdf
Executive Dashboard with plotly and Dash¶
In [ ]:
!pip install dash
!pip install dash_bootstrap_components
!pip install dash_html_components
!pip install dash_core_components
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Attacks Executive Summary
In [ ]:
# --- Imports ---
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
from dash import Dash, dcc, html, Input, Output
import dash_bootstrap_components as dbc
from plotly.subplots import make_subplots
# --- Data Loading ---
def load_data(filepath):
df = pd.read_csv(filepath)
df["Cost (M$)"] = df["Cost"] / 1_000_000
return df
# --- Utilities ---
def get_dropdown_options(df):
departments = sorted(df["Department Affected"].dropna().unique())
return [{'label': 'All', 'value': 'All'}] + [{'label': dept, 'value': dept} for dept in departments]
def get_top_n_options(df, max_n=20):
return [{'label': f'Top {i}', 'value': i} for i in range(1, min(len(df), max_n) + 1)]
# --- Data Extraction ---
def extract_core_metrics(df):
return {
"Total Issues": len(df),
"Critical Issues": len(df[df["Severity"] == "Critical"]),
"Resolved Issues": len(df[df["Status"].isin(["Resolved", "Closed"])]),
"Unresolved Issues": len(df[df["Status"].isin(["Open", "In Progress"])]),
}
def extract_attack_counts(df):
return {
"Phishing Attacks": len(df[df["Login Attempts"] > 10]),
"Malware Attacks": len(df[df["Num Files Accessed"] > 50]),
"DDOS Attacks": len(df[(df["Session Duration in Second"] > 7200) & (df["Data Transfer MB"] > 1000)]),
"Data Leak Attacks": len(df[df["Data Transfer MB"] > 500]),
"Insider Threats": len(df[df["Access Restricted Files"] == True]),
"Ransomware Attacks": len(df[df["CPU Usage %"] > 70]),
}
def get_attack_data_dict(df):
return {
"Phishing": df[df["Login Attempts"] > 10],
"Malware": df[df["Num Files Accessed"] > 50],
"DDOS": df[(df["Session Duration in Second"] > 7200) & (df["Data Transfer MB"] > 1000)],
"Data Leak": df[df["Data Transfer MB"] > 500],
"Insider Threats": df[df["Access Restricted Files"] == True],
"Ransomware Attacks": df[df["CPU Usage %"] > 70],
}
# --- Summary Builders ---
def build_summary_dict(df):
return {
"Total Attack": df.groupby("Threat Level").size(),
"Attack Volume Severity": df.groupby("Severity").size(),
"Impact in Cost(M$)": round(df.groupby("Severity")["Cost"].sum() / 1_000_000),
"Resolved Issues": df[df["Status"].isin(["Resolved", "Closed"])].groupby("Threat Level").size(),
"Outstanding Issues": df[df["Status"].isin(["Open", "In Progress"])].groupby("Threat Level").size(),
"Avg Response Time(Outstanding Issues)": round(
df[df["Status"].isin(["Open", "In Progress"])]
.groupby("Threat Level")["Issue Response Time Days"].mean()),
"Solved Issues Avg Response Time": round(
df[df["Status"].isin(["Resolved", "Closed"])]
.groupby("Threat Level")["Issue Response Time Days"].mean()),
}
# --- Chart Builders ---
def build_bar_chart(summary_dic):
colors = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b", "#e377c2"]
bar_fig = make_subplots(rows=3, cols=3, subplot_titles=list(summary_dic.keys()))
row, col = 1, 1
for i, (title, data) in enumerate(summary_dic.items()):
if data.empty: continue
sorted_data = data.sort_values()
bar_fig.add_trace(
go.Bar(
x=sorted_data.values, y=sorted_data.index.astype(str),
orientation='h', text=sorted_data.values, textposition='auto',
marker_color=colors[i % len(colors)]
), row=row, col=col)
col += 1
if col > 3: row += 1; col = 1
bar_fig.update_layout(height=700, title_text="Executive Metrics (Bar Charts)", showlegend=False)
bar_fig.update_xaxes(showgrid=False)
bar_fig.update_yaxes(showgrid=False)
bar_fig.update_xaxes(showticklabels=False)
bar_fig.update_yaxes(
showline=False,
ticks="",
showticklabels=True,
)
return bar_fig
def build_donut_chart(summary_dic):
donut_fig = make_subplots(rows=3, cols=3, specs=[[{'type': 'domain'}] * 3] * 3,
subplot_titles=list(summary_dic.keys()))
row, col = 1, 1
color_map = {"Critical": "darkred", "High": "red", "Medium": "orange", "Low": "green"}
for i, (title, data) in enumerate(summary_dic.items()):
if data.empty: continue
labels = data.index.astype(str)
values = data.values
colors_donut = [color_map.get(label, 'lightgray') for label in labels]
pull = [0.03] * len(labels) # slight pull for all slices
donut_fig.add_trace(
go.Pie(labels=labels, values=values, hole=0.4,
marker=dict(colors=colors_donut),
#textinfo='label+percent+value',
textinfo='none',
textposition='outside',
pull=pull,
texttemplate=["<br>%{label}<br>%{percent} (%{value})"] * len(labels),
insidetextfont=dict(size=10),
outsidetextfont=dict(size=10),),
row=row, col=col)
col += 1
if col > 3: row += 1; col = 1
donut_fig.update_layout(height=800, title_text="Executive Metrics (Donut Charts)", showlegend=False,
margin=dict(t=100, l=20, r=20, b=20),)
return donut_fig
def create_summary_bar(df, title, y_col, color_list, label):
df_sorted = df.sort_values(by=y_col, ascending=False)
fig = px.bar(df_sorted, x=df_sorted.index, y=y_col, title=title, labels={"index": label})
fig.update_traces(marker_color=color_list)
fig.update_layout(xaxis_title=label, yaxis_title=y_col, bargap=0.2, height=400, showlegend=False)
return fig
def create_bar_plot(df, title, x_col="Department Affected", y_col="Cost", top_n=None, bar_colors=None):
if df.empty:
return px.bar(title=f"{title}: No Data Available")
df = df.sort_values(by=y_col, ascending=False)
if top_n:
df = df.head(top_n)
if bar_colors:
colors_to_use = [bar_colors] if isinstance(bar_colors, str) else bar_colors
fig = px.bar(df, x=x_col, y=y_col, title=title, color_discrete_sequence=colors_to_use)
else:
fig = px.bar(df, x=x_col, y=y_col, color=x_col, title=title)
fig.update_layout(bargap=0.2, height=400, showlegend=False)
return fig
#--------tables-----------------------------
def get_department_filtered_df(df, selected_dept):
if selected_dept != "All":
return df[df["Department Affected"] == selected_dept]
return df
def get_top_n_issues(df, top_n):
return df.nlargest(top_n, "Threat Score")
def get_summary_statistics(df):
summary_dict = build_summary_dict(df)
return pd.DataFrame(summary_dict).apply(lambda x: round(x) if x.dtype.kind in 'biufc' else x)
def get_average_response_time(df):
avg_days = round(df["Issue Response Time Days"].fillna(0).mean())
return pd.DataFrame([{
"Average Response Time (Days)": avg_days,
"Average Response Time (Hours)": avg_days * 24,
"Average Response Time (Minutes)": avg_days * 1440
}])
def extract_issues_top_tables(df, top_n):
# round df column "Issue Response Time Days" value to zero decimal
df["Issue Response Time Days"] = df["Issue Response Time Days"].round(0)
top_base_df_ = get_top_n_issues(df, top_n)
top_base_df = top_base_df_[[
"Issue ID", "Threat Level", "Severity", "Issue Response Time Days",
"Department Affected", "Cost", "Defense Action", "Status"
]].copy()
top_critical_df = top_base_df[top_base_df["Severity"] == "Critical"]
top_resolved_df = top_base_df[top_base_df["Status"].isin(["Resolved", "Closed"])]
top_outstanding_df = top_base_df[top_base_df["Status"].isin(["In Progress", "Open"])]
return top_base_df, top_critical_df, top_resolved_df, top_outstanding_df
def create_table(df, title):
fig = go.Figure(data=[go.Table(
header=dict(values=list(df.columns), fill_color='lightblue', align='left'),
cells=dict(values=[df[col] for col in df.columns], fill_color='white', align='left')
)])
fig.update_layout(title=title, title_x=0.5)
return fig
#-----------------------------------------
# --- App Layout Builder ---
def build_layout(df, metrics_df, attacks_df, attack_data_dict):
return html.Div([
html.H1("Cyber Attacks Executive Dashboard", style={"textAlign": "center"}),
dcc.Tabs([
dcc.Tab(label='Metrics Charts', children=[
html.Div([
html.Div([
html.Label("Department Filter"),
dcc.Dropdown(id='exec-dept', options=get_dropdown_options(df), value='All')
], style={"width": "48%", "display": "inline-block"}),
html.Div([
html.Label("Top N Issues"),
dcc.Dropdown(id='exec-top-n', options=get_top_n_options(df), value=5)
], style={"width": "48%", "display": "inline-block", "float": "right"}),
dcc.Graph(id="bar-chart"),
dcc.Graph(id="donut-chart")
])
]),
dcc.Tab(label='Attack Summary', children=[
html.Div([
html.Div([
html.Label("Attack Type"),
dcc.Dropdown(
id="attack-type",
options=[{"label": "All", "value": "All"}] + [{"label": k, "value": k} for k in attack_data_dict],
value="All"
)
], style={"width": "30%", "display": "inline-block", "marginRight": "5%"}),
html.Div([
html.Label("Department"),
dcc.Dropdown(
id="attack-dept",
options=[{"label": "All", "value": "All"}] + [{"label": d, "value": d} for d in sorted(df["Department Affected"].dropna().unique())],
value="All"
)
], style={"width": "30%", "display": "inline-block", "marginRight": "5%"}),
#html.Div([
# html.Label("Top N"),
# html.Br(), html.Br(),
# dcc.Input(id="attack-top-n", type="number", value=10, min=1)
#], style={"width": "30%", "high": "30%" ,"display": "inline-block"}),
html.Div([
html.Label("Top N Issues"),
dcc.Dropdown(id="attack-top-n", options=get_top_n_options(df), value=5)
], style={"width": "28%", "display": "inline-block", "float": "right"}),
html.Div([
html.Div([dcc.Graph(id="attack-cost")], style={"width": "33%", "padding": "0 10px", "display": "inline-block"}),
html.Div([dcc.Graph(id="incident-summary")], style={"width": "33%", "padding": "0 10px", "display": "inline-block"}),
html.Div([dcc.Graph(id="attack-scenarios")], style={"width": "33%", "padding": "0 10px", "display": "inline-block"}),
], style={"display": "flex", "flexDirection": "row", "justifyContent": "space-between"})
])
]),
#----tables----
dcc.Tab(label='Tables', children=[
html.Div([
html.Label("Select Department Affected:"),
dcc.Dropdown(
id='department-dropdown',
options=get_dropdown_options(df),
value='All',
clearable=False
),
], style={'width': '48%', 'display': 'inline-block'}),
html.Div([
html.Label("Select Top N Issues by Cost:"),
dcc.Dropdown(
id='top-n-dropdown',
options=get_top_n_options(df),
value=5,
clearable=False
)
], style={'width': '48%', 'display': 'inline-block', 'float': 'right'}),
#html.Br(), html.Br(),
#---from here dcc instesd of dbc
html.Div([
dbc.Row([
dbc.Col(dcc.Graph(id='summary-table'), width=6),
])
], style={'width': '100%', 'display': 'inline-block'}),
html.Div([
dbc.Row([
dbc.Col(dcc.Graph(id='average-response-table'), width=6)
])
], style={'width': '60%', 'display': 'inline-block'}),
html.Div([
dbc.Row([
dbc.Col(dcc.Graph(id='top-issues-table'), width=12)
])
]),
html.Div([
dbc.Row([
dbc.Col(dcc.Graph(id='top-critical-issues-table'), width=12)
])
]),
html.Div([
dbc.Row([
dbc.Col(dcc.Graph(id='resolved-issues-table'), width=12)
])
]),
html.Div([
dbc.Row([
dbc.Col(dcc.Graph(id='outstanding-issues-table'), width=12)
])
])
#---------
])
])
])
# --- Callback Registration ---
def register_callbacks(app, df, attack_data_dict):
@app.callback(
Output("bar-chart", "figure"),
Output("donut-chart", "figure"),
Input("exec-dept", "value"),
Input("exec-top-n", "value")
)
def update_exec_charts(dept, top_n):
dff = df.copy()
if dept != "All":
dff = dff[dff["Department Affected"] == dept]
dff = dff.nlargest(top_n, "Threat Score")
summary = build_summary_dict(dff)
return build_bar_chart(summary), build_donut_chart(summary)
@app.callback(
Output("attack-cost", "figure"),
Output("incident-summary", "figure"),
Output("attack-scenarios", "figure"),
Input("attack-type", "value"),
Input("attack-dept", "value"),
Input("attack-top-n", "value")
)
def update_attack_charts(atype, dept, top_n, bar_colors='#FF5733'):
if atype == "All":
dff = pd.concat(attack_data_dict.values(), ignore_index=True)
else:
dff = attack_data_dict.get(atype, pd.DataFrame()).copy()
if dept != "All":
dff = dff[dff["Department Affected"] == dept]
# Dynamically determine bar_colors based on attack type or a default
if atype == "Phishing":
bar_colors = '#FF5733'
elif atype == "Malware":
bar_colors = '#33FF57'
elif atype == "DDOS":
bar_colors = '#3357FF'
elif atype == "Data Leak":
bar_colors = '#FF33A1'
elif atype == "Insider Threats":
bar_colors = '#A133FF'
elif atype == "Ransomware Attacks":
bar_colors = '#FFFF33'
else: # Default for "All" or other types
bar_colors = '#5733FF'
# Rebuild incident and attack scenarios summaries based on the filtered dff
incident_summary_df_filtered = pd.DataFrame(extract_core_metrics(dff), index=["Value"]).T
attack_scenarios_df_filtered = pd.DataFrame(extract_attack_counts(dff), index=["Value"]).T.dropna()
return ( create_bar_plot(dff, f"{atype} - Department vs Cost", top_n=top_n, bar_colors=bar_colors),
create_summary_bar(incident_summary_df_filtered, "Incident Summary", "Value", ['#636EFA']*len(incident_summary_df_filtered), "Metric"), # Adjust colors based on filtered data size
create_summary_bar(attack_scenarios_df_filtered, "Attack Scenarios", "Value", ['#FFA15A']*len(attack_scenarios_df_filtered), "Scenario") # Adjust colors based on filtered data size
)
#----table------
@app.callback(
Output('summary-table', 'figure'),
Output('average-response-table', 'figure'),
Output('top-issues-table', 'figure'),
Output('top-critical-issues-table', 'figure'),
Output('resolved-issues-table', 'figure'),
Output('outstanding-issues-table', 'figure'),
Input('department-dropdown', 'value'),
Input('top-n-dropdown', 'value')
)
def update_tables(selected_dept, top_n):
dept_df = get_department_filtered_df(df, selected_dept)
top_n_df = get_top_n_issues(dept_df, top_n)
summary_df = get_summary_statistics(dept_df)
avg_time_df = get_average_response_time(dept_df)
top_issues_df, top_critical_df, top_resolved_df, top_outstanding_df = extract_issues_top_tables(dept_df, top_n)
return (
create_table(summary_df.reset_index(), f"Executive Summary (Dept: {selected_dept})"),
create_table(avg_time_df, "Average Response Time (All Units)"),
create_table(top_issues_df, f"Top {top_n} Issues with Adaptive Defense (Dept: {selected_dept}"),
create_table(top_critical_df, f"Top {top_n} Critical Issues (Dept: {selected_dept})"),
create_table(top_resolved_df, f"Top {top_n} Resolved Issues (Dept: {selected_dept})"),
create_table(top_outstanding_df, f"Top {top_n} Outstanding Issues (Dept: {selected_dept})")
)
# --- Launcher ---
def launch_attacks_charts_dashboard():
file_path = "/content/drive/My Drive/Cybersecurity Data/combined_normal_and_simulated_attacks_class_df.csv"
df = load_data(file_path)
attack_data_dict = get_attack_data_dict(df)
metrics_df = pd.DataFrame.from_dict(extract_core_metrics(df), orient='index', columns=['Value'])
attacks_df = pd.DataFrame.from_dict(extract_attack_counts(df), orient='index', columns=['Value'])
app = Dash(__name__)
app.title = "Cyber Attack Summary Dashboard"
app.layout = build_layout(df, metrics_df, attacks_df, attack_data_dict)
register_callbacks(app, df, attack_data_dict)
app.run(debug=False, port=8051)
# --- Main ---
if __name__ == "__main__":
launch_attacks_charts_dashboard()